Climate Change AI Workshop Papers

On this page, we show accepted works from all our workshops on "Tackling Climate Change with Machine Learning."

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Venue	Title	Subject Areas
NeurIPS 2024	Time Series Viewmakers for Robust Disruption Prediction in Nuclear Fusion (Papers Track) Abstract and authors: (click to expand) Abstract: Tokamaks, as a leading technology in the quest for nuclear fusion energy, play a pivotal role in the fight against climate change. For tokamaks to become a viable solution for clean energy however, they must effectively detect and manage disruptions — plasma instabilities that can cause significant damages, impairing the reliability and efficiency required for their widespread adoption as a clean energy source. Machine learning (ML) models have shown promise in predicting disruptions for specific tokamaks, but they often struggle in generalizing to the diverse characteristics and dynamics of different machines. This limits the effectiveness of ML models across different tokamak designs and operating conditions, which is a critical barrier to scaling fusion technology. Given the success of data augmentation in improving model robustness and generalizability in other fields, this study explores the use of a novel time series viewmaker network to generate diverse augmentations or "views" of training data. Our results show that incorporating views during training improves AUC and F2 scores on DisruptionBench tasks compared to standard or no augmentations. This approach represents a promising step towards developing more broadly applicable ML models for disruption avoidance, which is essential for advancing fusion technology and, ultimately, addressing climate change through reliable and sustainable energy production. Authors: Dhruva Chayapathy (Alpharetta High School); Tavis Siebert (UC Berkeley); Lucas Spangher (Google); Cristina Rea (MIT Plasma Science and Fusion Center)	Power & Energy Time-series Analysis
NeurIPS 2024	Explainable Meta Bayesian Optimization with Human Feedback for Scientific Applications like Fusion Energy (Papers Track) Abstract and authors: (click to expand) Abstract: We introduce Meta Bayesian Optimization with Human Feedback (MBO-HF), which integrates Meta-Learning and expert preferences to enhance BO. MBO-HF employs Transformer Neural Processes (TNPs) to create a meta-learned surrogate model and a human-informed acquisition function (AF) to suggest and explain proposed candidate experiments. MBO-HF outperforms current methods in optimizing various scientific experiments and benchmarks in simulation, including the energy yield of the inertial confinement fusion (ICF), practical molecular optimization (PMO), and critical temperature maximization for superconducting materials. Authors: Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Rahman Ejaz (University of Rochester); Varchas Gopalaswamy (University of Rochester); Riccardo Betti (University of Rochester); Avisek Naug (Hewlett Packard Enterprise); Desik Rengarajan (Hewlett Packard Labs); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Paolo Faraboschi (Hewlett Packard Enterprise); Soumyendu Sarkar (Hewlett Packard Enterprise)	Power & Energy Reinforcement Learning
NeurIPS 2024	Enhancing Reinforcement Learning-Based Control of Wave Energy Converters Using Predictive Wave Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: Ocean wave energy is a reliable form of clean, renewable energy that has been under-explored compared to solar and wind. Wave Energy Converters (WEC) are devices that convert wave energy to electricity. To achieve a competitive Levelized Cost of Energy (LCOE), WECs require complex controllers to maximize the absorbed energy. Traditional engineering controllers, like spring-damper, cannot anticipate incoming waves, missing vital information that could lead to higher energy capture. Reinforcement Learning (RL) based controllers can instead optimize for long-term gains by being informed about the future waves. Prior works have utilized incoming wave information, achieving significant gains in energy capture. However, this has only been done via simulated waves (perfect prediction), making them impractical in real-life deployment. In this work, we develop a neural network based model for wave prediction. While prior works use auto-regressive techniques, we predict waves using information available on-device like position, acceleration, etc. We show that replacing the simulated waves with the wave predictor model can still maintain the gain in energy capture achieved by the RL controller in simulations. Authors: Vineet Gundecha (Hewlett Packard Enterpise); Arie Paap (Carnegie Clean Energy); mathieu Cocho (Carnegie Clean Energy); Sahand Ghorbanpour (Hewlett Packard Enterprise); Alexandre Pichard (Carnegie Clean Energy); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Soumyendu Sarkar (Hewlett Packard Enterprise)	Oceans & Marine Systems Power & Energy Reinforcement Learning Time-series Analysis
NeurIPS 2024	Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping (Papers Track) Abstract and authors: (click to expand) Abstract: The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by estimating solar potential from aerial imagery, but its impact is constrained by geographical coverage. This paper proposes expanding the API's reach using satellite imagery, enabling global solar potential assessment. We tackle challenges involved in building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Our models, trained on aligned satellite and aerial datasets, produce 25cm DSMs and roof segments. With ~1m DSM MAE on buildings, ~5deg roof pitch error and ~56% IOU on roof segmentation, they significantly enhance the Solar API's potential to promote solar adoption. Authors: Vishal Batchu (Google Research); Alex Wilson (Google); Betty Peng (Google); Carl Elkin (Google); Umangi Jain (University of Toronto); Christopher Arsdale (Google Research); Ross Goroshin (Google); Varun Gulshan (Google Research)	Earth Observation & Monitoring Buildings Cities & Urban Planning Power & Energy Computer Vision & Remote Sensing
NeurIPS 2024	Temporal sequence transformer to advance long-term streamflow prediction (Papers Track) Best ML Innovation Abstract and authors: (click to expand) Abstract: Accurate streamflow prediction is crucial for understanding climate change impacts on water resources and for effective management of extreme hydrological events. While Long Short-Term Memory (LSTM) networks have been the dominant data-driven approach for streamflow forecasting, recent advancements in transformer architectures for time series tasks have shown promise in outperforming traditional LSTM models. This study introduces a transformer-based model that integrates historical streamflow data with climatic variables to enhance streamflow prediction accuracy. We evaluated our transformer model against a benchmark LSTM across five diverse basins in the United States. Results demonstrate that the transformer architecture consistently outperforms the LSTM model across all evaluation metrics, highlighting its potential as a more effective tool for hydrological forecasting. This research contributes to the ongoing development of advanced AI techniques for improved water resource management and climate change adaptation strategies. Authors: Ruhaan Singh (Farragut High School); Dan Lu (Oak Ridge National Laboratory); Kshitij Tayal (Oak Ridge National Laboratory)	Time-series Analysis Climate Science & Modeling
NeurIPS 2024	Mapping Air Pollution Sources with Sequential Transformer Chaining: A Case Study in South Asia (Papers Track) Abstract and authors: (click to expand) Abstract: This study presents a comprehensive framework for detecting pollution sources, specifically factory and brick kiln chimneys, in major South Asian cities using a combination of remote sensing data and advanced deep learning techniques. We first identify hotspots of Acute Respiratory Infections (ARI) by correlating health data with air pollutant concentrations, including sulfur dioxide (SO_2), nitrogen dioxide (NO_2), and carbon monoxide (CO). For these identified hotspots, both low-resolution and high-resolution satellite imagery are acquired. Our approach employs a sequential process, beginning with a Vision Transformer model that utilizes high resolution satellite imagery and a broad range of text inputs with a lower confidence threshold to initially filter the data. This is followed by the application of the Remote CLIP model, which is run twice in succession using satellite imagery paired with different text inputs to refine the detection further. This sequential tranformer chaining filter out 99% of irrelevant data from high-resolution imagery. The final step involves manual annotation on the remaining 1% of the data, ensuring high accuracy and minimizing errors. Additionally, a novel multispectral chimney index is developed for detecting chimneys in low-resolution imagery. The study introduces a unique, annotated chimney detection dataset capturing diverse chimney types, which improves detection accuracy. The results provide actionable insights for public health interventions and support regulatory measures aimed at achieving the United Nations' Sustainable Development Goal 3 on health and well-being. We plan to make the dataset and code publicly available following the acceptance of this paper. Authors: Hafiz Muhammad Abubakar (Beaconhouse National University); Raahim Arbaz (Beaconhouse National University); Hasnain Ahmad (Beaconhouse National University); Mubasher Nazir (Solve Agri Pak Private Limited); Usman Nazir (Beaconhouse National University)	Climate Justice Health
NeurIPS 2024	Icy Waters: Developing a Test-Suite to Benchmark Sea Ice Concentration Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Artificial intelligence (AI) for Climate Change efforts have made significant progress in forecasting atmospheric weather patterns and events. Despite this, translating these gains in the context of phenomenon on earth surface, e.g. sea-ice concentration, has been limited because of differences in how these physical processes evolve. Sea ice concentration is one of the key indicators of climate change and is also critical for a number of different applications and indigenous peoples. Consequently, there is an acute need to develop a baseline of a diverse set of modern machine learning techniques within the Arctic. Our work aims to fill this gap, with the goal of both informing current research, as well as pointing out limitations with certain architectures. We achieve this by providing baselines for a number of different convolutional LSTMs, transformer based, and neural operator based machine learning methods. Authors: Kiernan McGuigan (University of Waterloo); Sirisha Rambhatla (University of Waterloo); K Andrea Scott (University of Waterloo)	Oceans & Marine Systems Time-series Analysis
NeurIPS 2024	A Deep Learning Approach to the Automated Segmentation of Bird Vocalizations from Weakly Labeled Crowd-sourced Audio (Papers Track) Abstract and authors: (click to expand) Abstract: Ecologists interested in monitoring the effects caused by climate change are increasingly turning to passive acoustic monitoring, the practice of placing autonomous audio recording units in ecosystems to monitor species richness and occupancy via species calls. However, identifying species calls in large datasets by hand is an expensive task, leading to a reliance on machine learning models. Due to a lack of annotated datasets of soundscape recordings, these models are often trained on large databases of community created focal recordings. A challenge of training on such data is that clips are given a "weak label," a single label that represents the whole clip. This includes segments that only have background noise but are labeled as calls in the training data, reducing model performance. Heuristic methods exist to convert clip-level labels to "strong" call-specific labels, where the label tightly bounds the temporal length of the call and better identifies bird vocalizations. Our work improves on the current weakly to strongly labeled method used on the training data for BirdNET, the current most popular model for audio species classification. We utilize an existing RNN-CNN hybrid, resulting in a precision improvement of 12% (going to 90% precision) against our new strongly hand-labeled dataset of Peruvian bird species. Authors: Jacob Ayers (Engineers for Exploration at UCSD); Sean Perry (University of California San Diego); Samantha Prestrelski (UC San Diego); Tianqi Zhang (Engineers for Exploration); Ludwig von Schoenfeldt (University of California San Diego); Mugen Blue (UC Merced); Gabriel Steinberg (Demining Research Community); Mathias Tobler (San Diego Zoo Wildlife Alliance); Ian Ingram (San Diego Zoo Wildlife Alliance); Curt Schurgers (UC San Diego); Ryan Kastner (University of California San Diego)	Earth Observation & Monitoring Ecosystems & Biodiversity
NeurIPS 2024	Enabling Adoption of Regenerative Agriculture through Soil Carbon Copilots (Papers Track) Abstract and authors: (click to expand) Abstract: Mitigating climate change requires transforming agriculture to minimize environmental impact and build climate resilience. Regenerative agricultural practices enhance soil organic carbon (SOC) levels, thus improving soil health and sequestering carbon. A challenge to increasing regenerative agriculture practices is cheaply measuring SOC over time and then understanding how SOC is affected by regenerative agricultural practices and other environmental factors and farm management practices. To address this challenge, we introduce an AI-driven Soil Organic Carbon Copilot that automates the ingestion of complex multi-resolution, multi-modal data to provide large-scale insights into soil health and regenerative practices. Our data includes extreme weather event data (e.g., drought conditions and wildfire incidents), farm management data (e.g., cropland information and tillage predictions), and SOC predictions. We find that integrating public data and specialized models enables large-scale, localized analysis for sustainable agriculture. In comparisons of agricultural activities and practices across California counties, we find evidence that diverse agricultural activity may mitigate the negative effects of tillage; and that while extreme weather conditions heavily affect SOC, composting may mitigate SOC loss. Finally, implementing role-specific personas empowers agronomists, farm consultants, policymakers, and other stakeholders to implement evidence-based strategies that promote sustainable agriculture and build climate resilience. Authors: Margaret Capetz (UCLA); Swati Sharma (Microsoft Research); Peder Olsen (Microsoft); RAFAEL PADILHA (Microsoft Research); Jessica Wolk (Microsoft); Emre Kiciman (Microsoft Research); Ranveer Chandra (Microsoft Research)	Carbon Capture & Sequestration Natural Language Processing
NeurIPS 2024	Carbon-Aware Spatio-Temporal Workload Distribution in Cloud Data Center Clusters Using Reinforcement Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs). In this paper, we introduce Green-DCC, which proposes Reinforcement Learning-based hierarchical controller techniques to dynamically optimize temporal and geographical workload distribution between data centers that belong to the same DCC. The environment models non-uniform external weather, carbon intensity, computing resources, cooling capabilities, and dynamic bandwidth costs, which provide constraints and interdependencies. We adapted and evaluated various reinforcement learning approaches, comparing their aggregate carbon emissions across the DCC, demonstrating Green-DCC's effectiveness for controlling and testing advanced data center control algorithms for sustainability. Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Antonio Guillen-Perez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Avisek Naug (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Sajad Mousavi (Hewlett Packard Enterprise); Paolo Faraboschi (Hewlett Packard Enterprise); Cullen Bash (Hewlett Packard Enterprise)	Power & Energy Reinforcement Learning
NeurIPS 2024	InvestESG: A Multi-agent Reinforcement Learning Benchmark for Studying Climate Investment as a Social Dilemma (Papers Track) Abstract and authors: (click to expand) Abstract: InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. Supported by both PyTorch and JAX implementation, the benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mitigation efforts and long-term benefits from reducing climate risk, while ESG-conscious investors attempt to influence corporate behavior through their investment decisions, in a scalable and hardware-accelerated manner. Companies allocate capital across mitigation, greenwashing, and resilience, with varying strategies influencing climate outcomes and investor preferences. Our experiments show that without ESG-conscious investors with sufficient capital, corporate mitigation efforts remain limited under the disclosure mandate. However, when a critical mass of investors prioritizes ESG, corporate cooperation increases, which in turn reduces climate risks and enhances long-term financial stability. Additionally, providing more information about global climate risks encourages companies to invest more in mitigation, even without investor involvement. Our findings align with empirical research using real-world data, highlighting MARL's potential to inform policy by providing insights into large-scale socio-economic challenges through efficient testing of alternative policy and market designs. Authors: Xiaoxuan Hou (University of Washington); Jiayi Yuan (University of Washington); Natasha Jaques (University of Washington)	Public Policy Climate Finance & Economics Reinforcement Learning
NeurIPS 2024	Exploring Vision Transformers for Early Detection of Climate Change Signals (Papers Track) Abstract and authors: (click to expand) Abstract: This study evaluates Vision Transformers (ViTs) for detecting anthropogenic climate change signals, crucial for effective policy planning and risk assessment. Compared to previously suggested models like CNN, MLP, and ridge regression, ViTs consistently detect forced climate signals earlier across three reanalysis datasets (ERA5, JRA-3Q, and MERRA-2). Interpretation with Integrated Gradients reveals consistent spatial patterns, suggesting ViTs utilize physically-grounded signals. This work highlights ViTs' potential to advance climate change detection and attribution tasks. Authors: Sungduk Yu (Intel Labs); Brian White (UNC Chapel Hill); Anahita Bhiwandiwalla (Intel Labs); Yaniv Gurwicz (Intel Labs); Musashi Hinck (Intel Corporation); Matthew Olson (Intel Labs); Raanan Rohekar (Intel Labs); Vasudev Lal (Intel Corp)	Climate Science & Modeling Earth Observation & Monitoring
NeurIPS 2024	No Location Left Behind: Introducing the Fairness Assessment for Implicit Representations of Earth Data (Papers Track) Abstract and authors: (click to expand) Abstract: Encoding and predicting physical measurements such as temperature or carbon dioxide is instrumental to many high-stakes challenges – including climate change. Yet, all recent advances solely assess models’ performances at a global scale. But while models’ predictions are improving on average over the entire globe, performances on sub-groups such as islands or coastal areas are left uncharted. To ensure safe deployment of those models, we thus introduce FAIR-Earth, a fine-grained evaluation suite made of diverse and high-resolution dataset. Our findings are striking–current methods produce highly biased predictions towards specific geospatial locations. The specifics of the biases vary based on the data modality and hyper-parameters of the models. Hence, we hope that FAIR-Earth will enable future research to design solutions aware of those per-group biases. Authors: Daniel Cai (Brown University); Randall Balestriero (Brown University)	Generative Modeling Climate Justice Climate Science & Modeling Earth Observation & Monitoring Extreme Weather Interpretable ML
NeurIPS 2024	Feasibility of Forecasting Highly Resolved Power Grid Frequency Utilizing Temporal Fusion Transformers (Papers Track) Abstract and authors: (click to expand) Abstract: As our society moves toward a decarbonized energy system, we need to improve our ability to model, predict, and understand power system behavior and dynamics. The balance between generation and demand on short time scales is reflected by the power grid frequency, making it central to the control of power grids. Hence, an accurate understanding and forecasting of power grid frequency could ease the planning of control actions and thus improve system stability and help save costs. Whether deep learning approaches can provide forecasts of the highly resolved and noisy time series, as they are present in the case of power grid frequency, remains an open question. In this paper, we find that the Temporal Fusion Transformer (TFT) is able to outperform baseline models, while a comparably simple multilayer perceptron is not. By reducing the time resolution of the frequency time series, we investigate and quantify the trade-off between the energy consumption and prediction performance % or forecasting accuracy? of the TFT. Furthermore, the inclusion of additional exogenous variables (e.g. calendar features, load, or generation) further improves the performance of the TFT. Utilizing the TFT's inherent interpretability, we identify the forecasted load ramp, the current hour, and the current month as the most relevant features. Authors: Hadeer El Ashhab (KIT); Benjamin Schäfer (Karlsruhe Institute of Technology); Sebastian Pütz (Karlsruhe Institute of Technology)	Power & Energy Time-series Analysis
NeurIPS 2024	A Water Efficiency Dataset for African Data Centers (Papers Track) Abstract and authors: (click to expand) Abstract: AI computing and data centers consume a large amount of freshwater, both directly for cooling and indirectly for electricity generation. While most attention has been paid to developed countries such as the U.S., this paper presents the first-of-its-kind dataset that combines nation-level weather and electricity generation data to estimate water usage efficiency for data centers in 41 African countries across five different climate regions. We also use our dataset to evaluate and estimate the water consumption of inference on two large language models (i.e., Llama-3-70B and GPT-4) in 11 selected African countries. Our findings show that writing a 10-page report using Llama-3-70B could consume about 0.7 liters of water, while the water consumption by GPT-4 for the same task may go up to about 60 liters. For writing a medium-length email of 120-200 words, Llama-3-70B and GPT-4 could consume about 0.13 liters and 3 liters of water, respectively. Interestingly, given the same AI model, 8 out of the 11 selected African countries consume less water than the global average, mainly because of lower water intensities for electricity generation. However, water consumption can be substantially higher in some African countries with a steppe climate than the U.S. and global averages, prompting more attention when deploying AI computing in these countries. Authors: NOAH SHUMBA (Carnegie Mellon University Africa); Opelo Tshekiso (Carnegie Mellon University Africa); Pengfei Li (UCR); Giulia Fanti (CMU); Shaolei Ren (UC Riverside)	Societal Adaptation & Resilience
NeurIPS 2024	Using Convolutional Neural Processes to Produce High-Resolution Weather Datasets Over New Zealand (Papers Track) Abstract and authors: (click to expand) Abstract: In recent years, there has been a surge in the development and success of artificial intelligence methods for global weather forecasting and climate modelling. For regional models, however, there is a lack of high-resolution datasets that can be used to train data-driven forecasting models. In this work, we showcase the use of convolutional neural processes (ConvNP) to generate hourly high-resolution (1km) weather datasets over New Zealand for temperature and precipitation. ConvNP models allow us to produce datasets that are enhanced by station observations and provide uncertainty estimates in their predictions. The generated datasets have applications in AI weather and climate models, model verification, and broader environmental research. Authors: Emily O'Riordan (Bodeker Scientific)	Meta- and Transfer Learning Climate Science & Modeling Extreme Weather Uncertainty Quantification & Robustness
NeurIPS 2024	AI-Driven Predictive Modeling of PFAS Contamination in Aquatic Ecosystems: Exploring A Geospatial Approach (Papers Track) Abstract and authors: (click to expand) Abstract: Per- and polyfluoroalkyl substances (PFAS), a class of synthetic fluorinated compounds termed “forever chemicals”, have garnered significant attention due to their persistence, widespread environmental presence, bioaccumulative properties, and associated risks for human health. Their presence in aquatic ecosystems highlights the link between human activity and the hydrological cycle. They also disrupt aquatic life, interfere with gas exchange, and disturb the carbon cycle, contributing to greenhouse gas emissions and exacerbating climate change. Federal agencies, state governments and non-government research and public interest organizations have emphasized the need for documenting the sites and the extent of PFAS contamination. However, the time-consuming and expensive nature of data collection and analysis poses challenges. It hinders the rapid identification of locations at high risk of PFAS contamination, which may then require further sampling or remediation. To address this data limitation, our study leverages a novel geospatial dataset, machine learning models including frameworks such as Random Forest, IBM-NASA's Prithvi and UNet, and geospatial analysis to predict regions with high PFAS concentrations in surface water. Using fish data from the National Rivers and Streams Assessment (NRSA) dataset by the Environmental Protection Agency (EPA), our analysis suggests the potential value of machine learning based models for targeted deployment of sampling investigations and remediation efforts. Authors: Jowaria Khan (University of Michigan); David Andrews (Environmental Working Group); Kaley Beins (Environmental Working Group); Sydney Evans (Environmental Working Group); Alexa Friedman (Environmental Working Group); Elizabeth Bondi-Kelly (MIT)	Computer Vision & Remote Sensing Earth Observation & Monitoring Climate Science & Modeling Ecosystems & Biodiversity Health Chemistry & Materials Data Mining Uncertainty Quantification & Robustness
NeurIPS 2024	End-to-End Conformal Calibration for Robust Grid-Scale Battery Storage Optimization (Papers Track) Abstract and authors: (click to expand) Abstract: The rapid proliferation of intermittent renewable electricity generation demands a corresponding growth in grid-scale energy storage systems to enable grid decarbonization. To encourage investment in energy storage infrastructure, storage operators rely on forecasts of electricity prices along with uncertainty estimates to maximize profit while managing risk. However, well-calibrated uncertainty estimates can be difficult to obtain in high-capacity prediction models such as deep neural networks. Moreover, in high-dimensional settings, there may be many valid uncertainty estimates with varied performance profiles—i.e., not all uncertainty is equally valuable for downstream decision-making. To address this challenge, this paper develops an end-to-end framework for conditional robust optimization, with robustness and calibration guarantees provided by conformal prediction. We represent arbitrary convex uncertainty sets with sublevel sets of partially input-convex neural networks, which are learned as part of our framework. We demonstrate the value of our approach for robust decision-making on a battery storage arbitrage application. Authors: Christopher Yeh (California Institute of Technology); Nicolas Christianson (California Institute of Technology); Adam Wierman (California Institute of Technology); Yisong Yue (Caltech)	Uncertainty Quantification & Robustness Power & Energy
NeurIPS 2024	AtmosArena: Benchmarking Foundation Models for Atmospheric Sciences (Papers Track) Abstract and authors: (click to expand) Abstract: Deep learning has emerged as a powerful tool for atmospheric sciences, showing significant utility across various tasks in weather and climate modeling. In line with recent progress in language and vision foundation models, there are growing efforts to scale and finetune such models for multi-task spatiotemporal reasoning. Despite promising results, existing works often evaluate their model on a small set of non-uniform tasks, which makes it hard to quantify broad generalization across diverse tasks and domains. To address this challenge, we introduce AtmosArena, the first multi-task benchmark dedicated to foundation models in atmospheric sciences. AtmosArena comprises a suite of tasks that cover a broad spectrum of applications in atmospheric physics and atmospheric chemistry. To showcase the capabilities and key features of our benchmark, we conducted extensive experiments to evaluate two state-of-the-art deep learning models, ClimaX and Stormer on AtmosArena, and compare their performance with other deep learning and traditional baselines. By providing a standardized, open-source benchmark, we aim to facilitate further advancements in the field, much like open-source benchmarks have driven the development of foundation models for language and vision. Authors: Tung Nguyen (University of California, Los Angeles); Prateik Sinha (UCLA); Advit Deepak (University of California, Los Angeles); Karen A. McKinnon (University of California, Los Angeles); Aditya Grover (UCLA)	Climate Science & Modeling Meta- and Transfer Learning
NeurIPS 2024	Harnessing AI for Wildfire Defense: An approach to Predict and Mitigate Global Fire Risk (Papers Track) Abstract and authors: (click to expand) Abstract: Wildfires pose a critical threat to wildlife, economies, properties, and human lives globally, making accurate risk assessment essential for effective management and mitigation. This study introduces a novel machine learning-based approach utilizing a Convolutional Neural Network (CNN) to evaluate wildfire risks across diverse ecosystems. Leveraging a comprehensive dataset of remote-sensed variables—including topography, vegetation health indicators, and climatic conditions—our model operates at a spatial resolution of 1000 meters per pixel, providing enhanced precision in predicting wildfire occurrences. The CNN outperforms state-of-the-art models, achieving a fire detection ratio of 0.82 and a no-fire detection ratio of 0.87. The results demonstrate that most dataset variables are crucial for accurate risk assessment, although some are non-essential. By integrating data from regions around the globe, this study underscores the feasibility and effectiveness of implementing globally scalable wildfire prediction tools. Authors: Hassan Ashfaq (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology)	Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring Ecosystems & Biodiversity Extreme Weather Forests Active Learning Generative Modeling Interpretable ML Reinforcement Learning
NeurIPS 2024	Tree Species Classification using Machine Learning and 3D Tomographic SAR - a case study in Northern Europe (Papers Track) Abstract and authors: (click to expand) Abstract: Tree species classification plays an important role in nature conservation, forest inventories, forest management, and the protection of endangered species. Over the past four decades, remote sensing technologies have been extensively utilized for tree species classification, with Synthetic Aperture Radar (SAR) emerging as a key technique. In this study, we employed TomoSense, a 3D tomographic dataset, which utilizes a stack of single-look complex (SLC) images, a byproduct of SAR, captured at different incidence angles to generate a three-dimensional representation of the terrain. Our research focuses on evaluating multiple tabular machine-learning models using the height information derived from the tomographic image intensities to classify eight distinct tree species. The SLC data and tomographic imagery were analyzed across different polarimetric configurations and geosplit configurations. We investigated the impact of these variations on classification accuracy, comparing the performance of various tabular machine-learning models and optimizing them using Bayesian optimization. Additionally, we incorporated a proxy for actual tree height using point cloud data from Light Detection and Ranging (LiDAR) to provide height statistics associated with the model’s predictions. This comparison offers insights into the reliability of tomographic data in predicting tree species classification based on height. Authors: Jumpei Takami (United Nations Office for Outer Space Affairs); Grace Colverd (University of Cambridge); Laura Schade (Department of Energy Security and Net Zero - UKGOV); Karol Bot (INESCTEC); Joseph Gallego (Drexel University)	Earth Observation & Monitoring Climate Science & Modeling
NeurIPS 2024	Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas (Papers Track) Abstract and authors: (click to expand) Abstract: This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to uncover latent charging profiles influenced by spatio-temporal factors. Our model focuses on peak power demand and daily load shapes, providing insights into charging behavior. Our results indicate significant impacts from the type of Basic Administrative Units on predicted load curves, which contributes to the understanding and optimization of EV charging infrastructure in urban settings and allows Distribution System Operators (DSO) to more efficiently plan EV charging infrastructure expansion. Authors: Marek Miltner (Stanford University; Czech Technical University); Jakub Zíka (CTU); Daniel Vašata (Czech Technical University in Prague, Faculty of Information Technology); Artem Bryksa (CTU); Magda Friedjungová (Czech Technical University in Prague, Faculty of Information Technology); Ondřej Štogl (CTU); Ram Rajagopal (Stanford University); Oldřich Starý (CTU)	Power & Energy Behavioral and Social Science Cities & Urban Planning Transportation Active Learning Data Mining Interpretable ML
NeurIPS 2024	High-Resolution Domestic Energy Modelling for National Energy and Retrofit Planning (Papers Track) Abstract and authors: (click to expand) Abstract: The UK's building stock, responsible for 13% of national greenhouse gas emissions in 2023, plays a crucial role in meeting the country's ambitious 2030 emissions reduction target. With the UK currently off-track and the building sector's emissions reductions slowing since 2014, there is an urgent need for improved energy modelling and policy development. We introduce a novel dataset for small-neighbourhood energy modelling in England and Wales to address this challenge. Covering 614k postcodes, ranging from 5-150 households, our dataset integrates domestic energy consumption data with variables spanning building characteristics, local environment, and socio-demographics. This dataset offers a new level of granularity in national energy analysis. It can provide insights for retrofit planning, material stock analysis and energy policy, transforming approaches to small-scale energy analysis and supporting the UK's climate goals. Authors: Grace Colverd (University of Cambridge); Ronita Bardhan (University of Cambridge); Jonathan Cullen (University of Cambridge)	Buildings Cities & Urban Planning
NeurIPS 2024	Continuous latent representations for modeling precipitation with deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: The sparse and spatio-temporally discontinuous nature of precipitation data presents significant challenges for simulation and statistical processing for bias correction and downscaling. These include incorrect representation of intermittency and extreme values (critical for hydrology applications), Gibbs phenomenon upon regridding, and lack of fine scales details. To address these challenges, a common approach is to transform the precipitation variable nonlinearly into one that is more malleable. In this work, we explore how deep learning can be used to generate a smooth, spatio-temporally continuous variable as a proxy for simulation of precipitation data. We develop a normally distributed field called pseudo-precipitation (PP) as an alternative for simulating precipitation. The practical applicability of this variable is investigated by applying it for downscaling precipitation from 1\degree (\(\sim\) 100 km) to 0.25\degree (\(\sim\) 25 km). Authors: Gokul Radhakrishnan (Verisk Analytics); Rahul Sundar (Verisk, India); Nishant Parashar (Verisk Analytics); Antoine Blanchard (Verisk); Daiwei Wang (Verisk Analytics); Boyko Dodov (Verisk Analytics)	Extreme Weather Generative Modeling
NeurIPS 2024	Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated Data (Papers Track) Abstract and authors: (click to expand) Abstract: CO2 emissions from power plants, as significant super emitters, contribute substantially to global warming. Accurate quantification of these emissions is crucial for effective climate mitigation strategies. While satellite-based plume inversion offers a promising approach, challenges arise from data limitations and the complexity of atmospheric conditions. This study addresses these challenges by (a) expanding the available dataset through the integration of NO2 data from Sentinel-5P, generating continuous XCO2 maps, and incorporating real satellite observations from OCO-2/3 for over 71 power plants in data-scarce regions; and (b) employing a customized U-Net model capable of handling diverse spatio-temporal resolutions for emission rate estimation. Our results demonstrate significant improvements in emission rate accuracy compared to previous methods [11]. By leveraging this enhanced approach, we can enable near real-time, precise quantification of major CO2 emission sources, supporting environmental protection initiatives and informing regulatory frameworks. Authors: Dibyabha Deb (Manipal Institute of Technology); Kamal Das (IBM Research)	Computer Vision & Remote Sensing Carbon Capture & Sequestration Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2024	Advancing Heat Demand Forecasting with Attention Mechanisms: Opportunities and Challenges (Papers Track) Abstract and authors: (click to expand) Abstract: Global leaders and policymakers are unified in their unequivocal commitment to decarbonization efforts in support of Net-Zero agreements. District Heating Systems (DHS), while contributing to carbon emissions due to the continued reliance on fossil fuels for heat production, are embracing more sustainable practices albeit with some sense of vulnerability as it could constrain their ability to adapt to dynamic demand and production scenarios. As demographic demands grow and renewables become the central strategy in decarbonizing the heating sector, the need for accurate demand forecasting has intensified. Advances in digitization have paved the way for Machine Learning (ML) based solutions to become the industry standard for modeling complex time series patterns. In this paper, we focus on building a Deep Learning (DL) model that uses deconstructed components of independent and dependent variables that affect heat demand as features to perform multi-step ahead forecasting of head demand. The model represents the input features in a time-frequency space and uses an attention mechanism to generate accurate forecasts. The proposed method is evaluated on a real-world dataset and the forecasting performance is assessed against LSTM and CNN-based forecasting models. Across different supply zones, the attention-based models outperforms the baselines quantitatively and qualitatively, with an Mean Absolute Error (MAE) of 0.105 0.06kWh and a Mean Absolute Percentage Error (MAPE) of 5.4% 2.8%, in comparison the second best model with a MAE of 21 0.10 0.06kWh and a MAPE of 5.6% 3%. Authors: Adithya Ramachandran (Pattern Recognition Lab, Friedrich Alexander University, Erlangen); Andreas Maier (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Siming Bayer (Pattern Recognition Lab, Friedrich-Alexander University)	Cities & Urban Planning Societal Adaptation & Resilience
NeurIPS 2024	AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: The growing computational demands of artificial intelligence (AI) in addressing climate change raise significant concerns about inefficiencies and environmental impact, as highlighted by Jevons' paradox. We propose an attention-enhanced quantum physics-informed neural network (AQ-PINN) to tackle these challenges. This approach integrates quantum computing techniques into physics-informed neural networks (PINNs) for climate modeling, aiming to enhance predictive accuracy in fluid dynamics governed by the Navier-Stokes equations while reducing the computational burden and carbon footprint. By harnessing variational quantum multi-head self-attention mechanisms, our AQ-PINN achieves a 51.51% reduction in model parameters compared to classical multi-head self-attention methods while maintaining comparable convergence and loss. It also employs quantum tensor networks to enhance representational capacity, which can lead to more efficient gradient computations and reduced susceptibility to barren plateaus. Our AQ-PINN represents a crucial step towards more sustainable and effective climate modeling solutions. Authors: Siddhant Dutta (SVKM's Dwarkadas J. Sanghvi College of Engineering); Nouhaila Innan (New York University Abu Dhabi); Sadok Ben Yahia (Southern Denmark University); Muhammad Shafique (New York University Abu Dhabi)	Climate Science & Modeling Carbon Capture & Sequestration Hybrid Physical Models
NeurIPS 2024	Enhancing Sustainability in Liquid-Cooled Data Centers with Reinforcement Learning Control (Papers Track) Abstract and authors: (click to expand) Abstract: The growing energy demands of machine learning workloads require sustainable data centers with lower carbon footprints and reduced energy consumption. Supercomputing and many high-performance computing (HPC) data centers, which use liquid cooling for greater efficiency than traditional air cooling systems, can significantly benefit from advanced optimization techniques to control liquid cooling. We present RL-LC, a novel Reinforcement Learning (RL) based approach designed to enhance the efficiency of liquid cooling in these environments. RL-LC integrates a customizable analytical liquid cooling model suitable for simulations or digital twins of data centers, focusing on minimizing energy consumption and carbon emissions. Our method achieves an average reduction of approximately 4% compared to industry-standard ASHRAE guidelines, contributing to more sustainable data center management and offering valuable insights for reducing the environmental impact of HPC operations. Authors: Avisek Naug (Hewlett Packard Enterprise); Antonio Guillen-Perez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Sajad Mousavi (Hewlett Packard Enterprise); Paolo Faraboschi (Hewlett Packard Enterprise); Cullen Bash (HPE); Soumyendu Sarkar (Hewlett Packard Enterprise)	Power & Energy Reinforcement Learning
NeurIPS 2024	Classification of Snow Depth Measurements for tracking plant phenological shifts in Alpine regions (Papers Track) Abstract and authors: (click to expand) Abstract: Ground-based snow depth measurements are often realized using ultrasonic or laser technologies, which by their nature measure the height of any underlying object, whether it is snow or vegetation in snow-free periods. We propose a machine learning approach to the automated classification of snow depth measurements into a snow cover class and a class corresponding to everything else, which takes into account both the temporal context and the dependencies between snow depth and other sensor measurements. Through a series of experiments we demonstrate that our approach simplifies the detection of seasonal snowmelt and corresponding onset of plant growth, which we used to assess climate-change related phenological shifts in otherwise rather poorly monitored high alpine regions. Authors: Jan Svoboda (WSL Institute for Snow and Avalanche Research SLF); Michael Zehnder (WSL Institute for Snow and Avalanche Research SLF); Marc Ruesch (WSL Institute for Snow and Avalanche Research SLF); David Liechti (WSL Institute for Snow and Avalanche Research SLF); Corinne Jones (Swiss Data Science Center); Michele Volpi (Swiss Data Science Center, ETH Zurich); Christian Rixen (WSL Institute for Snow and Avalanche Research SLF); Jürg Schweizer (WSL Institute for Snow and Avalanche Research SLF)	Climate Science & Modeling Earth Observation & Monitoring Ecosystems & Biodiversity Data Mining Time-series Analysis
NeurIPS 2024	Climate PAL: Climate Analysis through Conversational AI (Papers Track) Abstract and authors: (click to expand) Abstract: To support climate change research and its communication to the public, we propose Climate Projection and Analysis with Language models (Climate PAL). Our system allows users to retrieve and analyze climate projection data through conversational English. Using a crowdsourced evaluation dataset, we demonstrate that Climate PAL's retrieved data are more relevant to user queries, with over 20% higher accuracy than baselines on several key metrics. Authors: Sonia Cromp (University of Wisconsin-Madison); Behrad Rabiei (University of California San Diego); Maxwell Elling (University of Colorado Boulder); Alexander Herron (National Aeronautics and Space Administration); Michael Hendrickson (National Aeronautics and Space Administration)	Climate Science & Modeling Generative Modeling
NeurIPS 2024	Adaptive Policy Regularization for Offline-to-Online Reinforcement Learning in HVAC Control (Papers Track) Abstract and authors: (click to expand) Abstract: Reinforcement learning (RL)-based control methods have been extensively studied to improve building heating, ventilation, and air conditioning (HVAC) efficiency. Data-driven approaches demonstrate better transferability and scalability, making them useful in real-world applications. Most prior works focus on online learning requiring simulators or models of environment dynamics. However, transferring thermal simulators between environments is inefficient. We build on recent works that employ offline training on static datasets from unknown policies. Pure offline RL is constrained by the replay buffer's distribution, we propose using offline-to-online RL to enhance pre-trained offline models through online adaptation to distribution shifts. We show that direct online fine-tuning deteriorates performance on offline policies. To address this, we propose automatically tuning the actor's regularization during training to optimize the exploration-exploitation tradeoff. Specifically, we leverage simple moving averages of mean Q-values sampled throughout training. Simulation experiments demonstrate our method outperforms state-of-the-art approaches under various conditions, improving performance by 32.9% and enhancing pre-trained models' capabilities online. Authors: Hsin-Yu Liu (University of California San Diego); Bharathan Balaji (Amazon); Rajesh Gupta (UC San Diego); Dezhi Hong (Amazon)	Ecosystems & Biodiversity Climate Science & Modeling
NeurIPS 2024	Towards turbine-location-aware multi-decadal wind power predictions with CMIP6 (Papers Track) Abstract and authors: (click to expand) Abstract: With the increasing amount of renewable energy in the grid, long-term wind power forecasting for multiple decades becomes more critical. In these long-term forecasts, climate data is essential as it allows us to account for climate change. Yet the resolution of climate models is often very coarse. In this paper, we show that by including turbine locations when downscaling with Gaussian Processes, we can generate valuable aggregate wind power predictions despite the low resolution of the CMIP6 climate models. This work is a first step towards multi-decadal turbine-location-aware wind power forecasting using global climate model output. Authors: Nina Effenberger (University of Tübingen); Nicole Ludwig (University of Tübingen)	Power & Energy Climate Science & Modeling Hybrid Physical Models
NeurIPS 2024	Equity-Aware Spatial-Temporal Workload Shifting for Sustainable AI Data Centers (Papers Track) Abstract and authors: (click to expand) Abstract: The escalated demand for hyperscale data centers due to generative AI, has intensified the operational load, leading to increased energy consumption, water usage, and carbon emissions. We propose EquiShift, a novel equitable spatial-temporal workload balancing algorithm that shifts workloads spatially and temporarily across geographically different data centers to minimize the overall energy costs while ensuring fair distribution of water and carbon footprints. Concretely, EquiShift introduces a model predictive control (MPC) framework to solve the equitable load balancing problem, leveraging the predictive capabilities of MPC to optimize load distribution in real-time. Finally, we present comparative evaluations against state-of-the-art load-balancing algorithms to demonstrate the performance of EquiShift which underscores the potential of equitable load balancing as a key strategy for enhancing the sustainability of data centers while achieving fairness in the face of growing computational demands. Authors: Mohammad Islam (University of California Riverside); Shaolei Ren (UC Riverside)	Power & Energy
NeurIPS 2024	Climate Impact Assessment Requires Weighting: Introducing the Weighted Climate Dataset (Papers Track) Abstract and authors: (click to expand) Abstract: High-resolution gridded climate data are readily available from multiple sources, yet climate research and decision-making increasingly require country and region-specific climate information weighted by socio-economic factors. Moreover, the current landscape of disparate data sources and inconsistent weighting methodologies exacerbates the reproducibility crisis and undermines scientific integrity. To address these issues, we have developed a globally comprehensive dataset at both country (GADM0) and region (GADM1) levels, encompassing various climate indicators (precipitation, temperature, SPEI, wind gust). Our methodology involves weighting gridded climate data by population density, night-time light intensity, cropland area, and concurrent population count – all proxies for socio-economic activity – before aggregation. We process data from multiple sources, offering daily, monthly, and annual climate variables spanning from 1900 to 2023. A unified framework streamlines our preprocessing steps, and rigorous validation against leading climate impact studies ensures data reliability. The resulting Weighted Climate Dataset is publicly accessible through an online dashboard at https://weightedclimatedata.streamlit.app/. Authors: Marco Gortan (University of Basel); Lorenzo Testa (Carnegie Mellon University); Giorgio Fagiolo (Sant'Anna School of Advanced Studies); Francesco Lamperti (Sant'Anna School of Advanced Studies)	Extreme Weather Public Policy Societal Adaptation & Resilience Uncertainty Quantification & Robustness
NeurIPS 2024	Meta-Learned Bayesian Optimization for Energy Yield in Inertial Confinement Fusion (Papers Track) Abstract and authors: (click to expand) Abstract: With the growing demand for clean energy, fusion presents a promising path to sustainable power generation. Inertial confinement fusion (ICF) experiments trigger nuclear reactions by firing lasers at a fuel target, typically composed of deuterium and tritium. These experiments are costly and require complex optimization of the laser pulse shape across multiple shots to maximize energy yield. Even though Bayesian Optimization (BO) has been commonly used to optimize such expensive scientific experiments, vanilla BO methods do not leverage prior knowledge of the function from simulations or past experiments and fail to achieve high sample efficiency. In this work, we adapted and explored BO meta-learning techniques for ICF that either meta-learn the BO surrogate model, the acquisition function, or both from simulations. Our results demonstrate that the three meta-learning techniques we investigated, Meta-Learning Acquisition Functions for BO (MetaBO), Rank-Weighted Gaussian Process Ensemble (RGPE), and Neural Acquisition Processes (NAP), drastically reduce the number of experiments needed to achieve a satisfactory yield in ICF simulations. Authors: Vineet Gundecha (Hewlett Packard Enterpise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Rahman Ejaz (University of Rochester); Varchas Gopalaswamy (University of Rochester); Riccardo Betti (University of Rochester); Avisek Naug (Hewlett Packard Enterprise); Paolo Faraboschi (Hewlett Packard Enterprise); Soumyendu Sarkar (Hewlett Packard Enterprise)	Power & Energy Meta- and Transfer Learning
NeurIPS 2024	WildfireGPT: Tailored Large Language Model for Wildfire Analysis (Papers Track) Abstract and authors: (click to expand) Abstract: Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-specific information, particularly in areas requiring specialized knowledge, such as wildfire details within the broader context of climate change. For decision-makers focused on wildfire resilience and adaptation, it is crucial to obtain responses that are not only precise but also domain-specific. To that end, we developed WildfireGPT, a prototype LLM agent designed to transform user queries into actionable insights on wildfire risks. We enrich WildfireGPT by providing additional context, such as climate projections and scientific literature, to ensure its information is current, relevant, and scientifically accurate. This enables WildfireGPT to be an effective tool for delivering detailed, user-specific insights on wildfire risks to support a diverse set of end users, including but not limited to researchers and engineers, for making positive impact and decision making. Authors: Yangxinyu Xie (University of Pennsylvania); Bowen Jiang (University of Pennsylvania); Tanwi Mallick (Argonne National Laboratory); Joshua Bergerson (Argonne National Laboratory); John Hutchison (Argonne National Laboratory); Duane Verner (Argonne National Laboratory); Jordan Branham (Argonne National Laboratory); M. Ross Alexander (Argonne National Laboratory); Robert Ross (Argonne National Laboratory); Yan Feng (Argonne National Laboratory); Leslie-Anne Levy (Argonne National Laboratory); Weijie Su (University of Pennsylvania); Camillo Jose Taylor (University of Pennsylvania)	Disaster Management and Relief Societal Adaptation & Resilience Recommender Systems
NeurIPS 2024	Methane SatMapper: Methane Detection from Satellite Imagery Using Hyperspectral Transformer (Papers Track) Abstract and authors: (click to expand) Abstract: Methane (CH4) plays a critical role in accelerating global climate change, and recent advancements using Sentinel-2 satellite imagery have demonstrated potential in detecting and quantifying significant methane emissions. However, existing approaches often rely on temporal analysis of shortwave-infrared spectra, assuming consistent ground conditions and prior knowledge of methane-free periods, which can lead to errors and limit scalability. To overcome these challenges, we present Methane SatMapper, an innovative end-to-end spectral transformer model specifically designed to accurately identify and quantify methane plumes. Our model introduces two novel modules: one that identifies potential methane emission sites by analyzing solar radiation absorption in the spectral domain and another that localizes and quantifies methane plumes without the need for temporal data. By utilizing all 12 spectral channels of Sentinel-2 imagery, our architecture effectively estimates ground terrain and detects methane emissions, providing enhanced robustness to variable ground conditions and increased computational efficiency by eliminating the need for historical time-series data. Primary evaluations confirm that Methane SatMapper delivers precise and reliable methane detection, addressing key limitations in scalability and temporal dependence. Authors: Satish Kumar (University of California, Santa Barbara); ASM Iftekhar (Microsoft); Bowen Zhang (University Of California, Santa Barbara); Richard Sserunjogi (Makerere University); Mehan Jayasuriya (Mozilla Technology Foundation)	Earth Observation & Monitoring Climate Science & Modeling Causal & Bayesian Methods Computer Vision & Remote Sensing
NeurIPS 2024	A Low-Complexity Data-Driven Algorithm for Residential PV-Storage Energy Management (Papers Track) Abstract and authors: (click to expand) Abstract: This paper uses the principles of online convex learning to propose a momentum-optimized smart (MOS) controller for energy management of residential PV-storage systems. Using the self-consumption-maximization application and practical data, the method's performance is compared to classical rolling-horizon quadratic programming. Findings support online learning methods for residential applications given their low complexity and small computation, communication, and data footprint. Consequences include improved economics for residential PV-storage systems and mitigation of distribution systems' operational challenges associated with high PV penetration. Authors: Mostafa Farrokhabadi (University of Calgary)	Power & Energy Time-series Analysis
NeurIPS 2024	Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen (Papers Track) Abstract and authors: (click to expand) Abstract: Due to climate change the frequency and intensity of extreme rainfall events, which contribute to urban flooding, are expected to increase in many places. These floods can damage transport infrastructure and disrupt mobility, highlighting the need for cities to adapt to escalating risks. Reinforcement learning (RL) serves as a powerful tool for uncovering optimal adaptation strategies, determining how and where to deploy adaptation measures effectively, even under significant uncertainty. In this study, we leverage RL to identify the most effective timing and locations for implementing measures, aiming to reduce both direct and indirect impacts of flooding. Our framework integrates climate change projections of future rainfall events and floods, models city-wide motorized trips, and quantifies direct and indirect impacts on infrastructure and mobility. Preliminary results suggest that our RL-based approach can significantly enhance decision-making by prioritizing interventions in specific urban areas and identifying the optimal periods for their implementation. Our framework is publicly available: \url{https://github.com/MLSM-at-DTU/floods_transport_rl}. Authors: Miguel Costa (Technical University of Denmark); Morten W. Petersen (Technical University of Denmark); Arthur Vandervoort (Technical University of Denmark); Martin Drews (Technical University of Denmark); Karyn Morrissey (Technical University of Denmark); Francisco Pereira (DTU)	Transportation Reinforcement Learning
NeurIPS 2024	Learning the Indicators of Energy Burden for Knowledge Informed Policy (Papers Track) Abstract and authors: (click to expand) Abstract: The United States is one of the largest energy consumers per capita, which puts an expectation on households to have adequate energy expenditures to keep up with modern society. This adds additional stress on low-income households that may need to limit energy use due to financial constraints. This paper investigates energy burden, the ratio of household energy bills to household income, within the United States West. Self-Organizing Maps, an unsupervised neural network, is used to learn the indicators attributed to energy burden to inform public policy. This is one of the first studies to consider environmental justice indicators, which include outdoor air quality metrics and health disparities as energy burden indicators. The results show significant (p<0.05) differences among high energy burden areas and those with no energy burden for the environmental justice indicators. Thus, beyond the socioeconomic hardships of marginalized communities, counties with high energy burden suffer from environmental and health hazards, which will be amplified under a changing climate. Authors: Jasmine Garland (University of Colorado Boulder); Rajagopalan Balaji (University of Colorado, Boulder); Kyri Baker (University of Colorado, Boulder); Ben Livneh (University of Colorado, Boulder)	Public Policy Behavioral and Social Science Climate Justice Health Unsupervised & Semi-Supervised Learning
NeurIPS 2024	Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers (Papers Track) Abstract and authors: (click to expand) Abstract: One of the major sources of uncertainty in the current generation of Global Climate Models (GCMs) is the simulation of sub-grid scale physical processes. Over the years, with significantly improved computational performance, a series of Deep Learning (DL) parameterization schemes have been developed and incorporated into GCMs. However, these schemes use classic architectures whereas the new attention mechanism is not widely investigated. We proposed a “memory-aware” transformer-based model on ClimSim, the largest-ever dataset for climate parameterization. Our results show that the attention mechanism successfully captures the complex non-linear dependencies of sub-grid scale variables and reduces the prediction error. Authors: Shuochen Wang (Northeastern University); Nishant Yadav (Microsoft); Auroop Ganguly (Northeastern University)	Climate Science & Modeling
NeurIPS 2024	Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate weather and climate modeling are critical for both scientific advancement and safeguarding communities against environmental risks. Traditional approaches rely heavily on Numerical Weather Prediction (NWP) models, which simulate energy and matter flow across Earth's systems. However, heavy computational requirements and low efficiency restrict the suitability of NWP, leading to a pressing need for enhanced modeling techniques. Neural network-based models have emerged as promising alternatives, leveraging data-driven approaches to forecast atmospheric variables. In this work, we focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. As a case study, we consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. This targeted approach allows us to tailor the model's capabilities to the unique conditions of the region of interest. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions. Our codebase and pre-trained models can be accessed at \url{https://github.com/akhtarvision/weather-regional}. Authors: Muhammad Akhtar Munir (MBZUAI); Fahad Shahbaz (MBZUAI); Salman Khan (MBZUAI)	Climate Science & Modeling Earth Observation & Monitoring
NeurIPS 2024	DivShift: Exploring Domain-Specific Distribution Shift in Large-Scale, Volunteer-Collected Biodiversity Datasets (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is negatively impacting the world's biodiversity. To build automated systems to monitor these negative biodiversity impacts, large-scale, volunteer-collected datasets like iNaturalist are built from community-identified, natural imagery. However, such volunteer-based data are opportunistic and lack a structured sampling strategy, resulting in geographic, temporal, observation quality, and socioeconomic, biases that stymie uptake of these models for downstream biodiversity monitoring tasks. Here we introduce DivShift North American West Coast (DivShift-NAWC), a curated dataset of almost 8 million iNaturalist plant images across the western coast of North America, for exploring the effects of these biases on deep learning model performance. We compare model performance across four known biases and observe that they indeed confound model performance. We suggest practical strategies for curating datasets to train deep learning models for monitoring climate change's impacts on the world's biodiversity. Authors: Elena Sierra (Stanford University); Lauren Gillespie (Stanford University); Salim Soltani (University of Freiburg); Moisés Expósito-Alonso (University of California, Berkeley); Teja Kattenborn (University of Freiburg)	Ecosystems & Biodiversity Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2024	RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models (Papers Track) Abstract and authors: (click to expand) Abstract: This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at https://github.com/p3jitnath/climate-rl. Authors: Pritthijit Nath (University Of Cambridge); Henry Moss (Secondmind); Emily Shuckburgh (University Of Cambridge); Mark Webb (Met Office)	Climate Science & Modeling Hybrid Physical Models Reinforcement Learning
NeurIPS 2024	Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: In response to climate change, assessing crop productivity under extreme weather conditions is essential to enhance food security. Crop simulation models, which align with physical processes, offer explainability but often perform poorly. Conversely, machine learning (ML) models for crop modeling are powerful and scalable yet operate as black boxes and lack adherence to crop growth’s physical principles. To bridge this gap, we propose a novel method that combines the strengths of both approaches by estimating the water use and the crop sensitivity to water scarcity at the pixel level. This approach enables yield loss estimation grounded in physical principles by sequentially solving the equation for crop yield response to water scarcity, using an enhanced loss function. Leveraging Sentinel-2 satellite imagery, climate data, simulated water use data, and pixel-level yield data, our model demonstrates high accuracy, achieving an $R^2$ of up to 0.77—matching or surpassing state-of-the-art models like RNNs and Transformers. Additionally, it provides interpretable and physical consistent outputs, supporting industry, policymakers, and farmers in adapting to extreme weather conditions. Authors: Miro Miranda (University of Kaiserslautern-Landau); Marcela Charfuelan (German Research Center for Artificial Intelligence); Andreas Dengel (DFKI GmbH)	Agriculture & Food Hybrid Physical Models
NeurIPS 2024	Modeling Pollution Spread with Obstructions using Physics-Informed Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Pollution modeling plays a crucial role in combating climate change and protecting public health. Scientists rely on traditional numerical methods, such as finite difference and computational fluid dynamics, for accurate pollution simulations; however, they are resource-intensive and time-consuming often taking days to execute. Physics-Informed Neural Networks (PINNs) present a promising alternative, capable of significantly improving speed while maintaining high accuracy. In our research, we developed a PINN to model pollution spread under laminar flow conditions in a two-dimensional environment with obstructions and reflecting boundaries. Our model integrates the Navier-Stokes and advection-diffusion partial differential equations (PDEs) and enforces zero-flux Neumann boundaries at obstruction surfaces and simulation edges. We used a hybrid learning approach, generating a dataset of 6.1 million colocation points for supervised learning and embedding physical laws into the loss function for unsupervised learning. In our experiments, the PINN was over 2520 times faster than the traditional numerical solver, returning results in under 2190 milliseconds and achieving a mean squared error below 3E-5. Our findings demonstrate that PINNs not only offer a drastic reduction in computational time but also scale favorably with both the time domain and spatial resolution, making them a viable solution for real-time pollution monitoring and emergency response planning. Future work will focus on extending our model to dynamic obstructions and arbitrary grid geometries. Authors: Yash Ranjith (Westmont)	Climate Science & Modeling Hybrid Physical Models Unsupervised & Semi-Supervised Learning
NeurIPS 2024	Prediction of PM2.5 concentration based on ARIMA, LSTM, and TCN models in Kigali, Rwanda (Papers Track) Abstract and authors: (click to expand) Abstract: PM2.5 pollution is a major health concern, especially in areas lacking robust real-time monitoring and predictive capabilities. This study presents a comparative analysis of three forecasting models—Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)—to predict PM2.5 concentrations in four regions of Kigali, Rwanda. Utilizing a dataset spanning from late 2020 to 2024, these models were trained on historical air quality data obtained from sensors. Our findings reveal that the LSTM model consistently outperforms both TCN and ARIMA models, delivering lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) in predicting PM2.5 levels. These results underscore the effectiveness of LSTM models in providing more accurate air quality forecasts in complex temporal environments. This research lays the groundwork for enhancing air quality monitoring and public health strategies in Rwanda. Authors: Yamlak Bogale (Carnegie Mellon University Africa); Choukouriyah Arinloye (Carnegie Mellon University Africa); Joselyne Muragijemariya (Carnegie Mellon University Africa)	Time-series Analysis Climate Science & Modeling
NeurIPS 2024	Wildflower Monitoring with Expert-annotated Images and Flowering Phenology (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding biodiversity trends is essential for preservation policy planning, and advanced computer vision solutions now enable large-scale automated monitoring for many biodiversity use cases. Wildflower monitoring, in particular, presents unique challenges. Visual similarities in shape and color may exist between different species, while flowers within a species may have significant visual differences. Moreover, flowers follow a growth cycle and look distinctly different over the year, while different species flower at different times of the year. Having access to flowering phenology, more accurate predictions may be made. We propose a novel multi-modal wildflower monitoring task to better identify species, levering both expert-annotated wildflower images and flowering phenology estimates. Moreover, we benchmark several state-of-the-art models using two groups of common wildflower species that have high inter-class similarity, and show that this multi-modal approach significantly outperforms image-only baselines. With this work, we aim to encourage the development of standards for automated wildflower monitoring as a step towards bending the curve of biodiversity loss. The data and the code are publicly available https://georgianagmanolache.github.io/wildflowerpower/ Authors: Georgiana Manolache (Fontys University of Applied Science); Gerard Schouten (Fontys University of Applied Sciences)	Ecosystems & Biodiversity Public Policy Computer Vision & Remote Sensing Meta- and Transfer Learning
NeurIPS 2024	Light-weight geospatial model for global deforestation attribution (Papers Track) Abstract and authors: (click to expand) Abstract: Forests are in decline worldwide and it is critical to attribute forest cover loss to its causes. We gathered a curated global dataset of all forest loss drivers and developed a neural network model to recognize the main drivers of deforestation or forest degradation at 1-km scale. Using remote sensing satellite data together with ancillary biophysical and socioeconomic data the model estimates the dominant drivers of forest loss from 2001 to 2022. Using a relatively light-weight geospatial model allowed us to to train a single world-wide model. We generated a global map of drivers of forest loss that is being validated, and present the first insights such data can provide. Authors: Anton Raichuk (Google); Michelle Sims (WRI); Radost Stanimirova (WRI); Maxim Neumann (Google)	Earth Observation & Monitoring Climate Science & Modeling Ecosystems & Biodiversity Forests Computer Vision & Remote Sensing
NeurIPS 2024	HVAC-DPT: A Decision Pretrained Transformer for HVAC Control (Papers Track) Abstract and authors: (click to expand) Abstract: Building operations consume approximately 40% of global energy, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for up to 50% of this consumption [1, 2]. As HVAC energy demands are expected to rise, optimising system efficiency is crucial for reducing future energy use and mitigating climate change [3]. Existing control strategies lack generalisation and require extensive training and data, limiting their rapid deployment across diverse buildings. This paper introduces HVAC-DPT, a Decision-Pretrained Transformer using in-context Reinforcement Learning (RL) for multi-zone HVAC control. HVAC-DPT frames HVAC control as a sequential prediction task, training a causal transformer on inter- action histories generated by diverse RL agents. This approach enables HVAC-DPT to refine its policy in-context, without modifying network parameters, allowing for deployment across different buildings without the need for additional training or data collection. HVAC-DPT reduces energy consumption in unseen buildings by 45% compared to the baseline controller, offering a scalable and effective approach to mitigating the increasing environmental impact of HVAC systems. Authors: Anaïs Berkes (University of Cambridge)	Buildings Generative Modeling Meta- and Transfer Learning Reinforcement Learning
NeurIPS 2024	Regional Ocean Forecasting with Hierarchical Graph Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate ocean forecasting systems are vital for understanding marine dynamics, which play a crucial role in environmental management and climate adaptation strategies. Traditional numerical solvers, while effective, are computationally expensive and time-consuming. Recent advancements in machine learning have revolutionized weather forecasting, offering fast and energy-efficient alternatives. Building on these advancements, we introduce SeaCast, a neural network designed for high-resolution, medium-range ocean forecasting. SeaCast employs a graph-based framework to effectively handle the complex geometry of ocean grids and integrates external forcing data tailored to the regional ocean context. Our approach is validated through experiments at a high spatial resolution using the operational numerical model of the Mediterranean Sea provided by the Copernicus Marine Service, along with both numerical and data-driven atmospheric forcings. Authors: Daniel Holmberg (University of Helsinki); Emanuela Clementi (CMCC Foundation); Teemu Roos (University of Helsinki)	Oceans & Marine Systems Climate Science & Modeling
NeurIPS 2024	Parakeet: Emission Factor Recommendation for Carbon Footprinting with Generative AI (Papers Track) Abstract and authors: (click to expand) Abstract: Accurately quantifying greenhouse gas (GHG) emissions from products and business activities is crucial for organizations to measure their environmental impact and undertake mitigation actions. Life cycle assessment (LCA) is the scientific discipline for measuring GHG emissions associated with each stage of a product or activity, from raw material extraction to disposal. Measuring the emissions outside of a product owner's control is challenging, and practitioners rely on emission factors (EFs) – estimates of GHG emissions per unit of activity – to model and estimate indirect impacts. These EFs come from prior LCA studies and are collated into databases. The current practice of manually finding the appropriate EF to use from databases is time-consuming, error-prone, and requires domain expertise, hindering scalability and accuracy in emissions quantification. We present a novel AI-assisted method that leverages large language models to automatically recommend EFs. Our method parses business activity descriptions and recommends the appropriate EF with a human-interpretable justification. We benchmark our solution across multiple domains and find it achieves state-of-the-art performance in EF recommendation, with an average Precision@1 of 88.4%. By streamlining and automating the EF selection process, our AI-assisted method enables scalable and accurate quantification of GHG emissions, supporting organizations' sustainability initiatives and driving progress toward net-zero emissions targets across industries. Authors: Bharathan Balaji (Amazon); Nina Domingo (Amazon); Abu Zaher Faridee (Amazon); Venkata Sai Gargeya Vunnava (amazon); Anran Wang (Amazon); Fahimeh Ebrahimi Meymand (Amazon); Kellen Axten (Amazon); Aravind Srinivasan (Amazon); Qingshi Tu (University of British Columbia); Harsh Gupta (Amazon); Shikha Gupta (Amazon); Soma Ramalingam (Amazon); Jeremie Hakian (Amazon); Jared Kramer (Amazon)	Climate Finance & Economics Natural Language Processing Recommender Systems
NeurIPS 2024	Towards CLEAN: Cycle Learning Energy-Emission Assessment Network (Papers Track) Abstract and authors: (click to expand) Abstract: Formulating effective climate policies is challenging due to the limitations of current decision-making models, such as Computable General Equilibrium models, which often rely on static, linear assumptions that oversimplify complex, real-world scenarios. To address these limitations, we introduce the Cycle Learning Energy-Emission Assessment Network, a scalable deep-learning framework trained on the Carbon Monitor Energy-Emission time series dataset. CLEAN captures global patterns between energy structures and sectoral carbon emissions, enabling accurate energy-emission predictions at national, regional, city, and sectoral levels. By framing the problem as a bidirectional supervised regression task, our model achieves a Mean Absolute Percentage Error of approximately 5%, significantly outperforming traditional models like XGBoost, especially with our novel data augmentation method, Densify. This demonstrates CLEAN’s superior performance and generalization capabilities, making it a powerful tool for climate policy analysis. The CMEE dataset, CLEAN model, and Densify augmentation methods are open-sourced at \url{https://anonymous.4open.science/r/CLEAN-D66A}. Authors: Yanming Guo (University of Sydney); Jin Ma (University of Sydney); Kevin Credit (Maynooth University); Qian Xiao (Maynooth University)
NeurIPS 2024	Multi-Source Temporal Attention Network for Precipitation Nowcasting (Papers Track) Best Pathway to Impact Abstract and authors: (click to expand) Abstract: Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 hours in advance with greater accuracy than existing operational physics-based and extrapolation-based models. Our model leverages multi-source meteorological data and physics-based forecasts to deliver high-resolution predictions in both time and space. It captures complex spatio-temporal dynamics through temporal attention networks and is optimized using data quality maps and dynamic thresholds. Experiments demonstrate that our model outperforms state-of-the-art, and highlight its potential for fast reliable responses to evolving weather conditions. Authors: Rafael Pablos Sarabia (Aarhus University & Cordulus); Joachim Nyborg (Cordulus); Morten Birk (Cordulus); Jeppe Liborius Sjørup (Cordulus); Anders Lillevang Vesterholt (Cordulus); Ira Assent (Aarhus University)	Computer Vision & Remote Sensing Earth Observation & Monitoring Extreme Weather
NeurIPS 2024	Critical misalignments between climate action and sustainable development goals revealed (Papers Track) Abstract and authors: (click to expand) Abstract: A mere 12 percent of the Sustainable Development Goals (SDGs) is currently on track to meet the 2030 deadline in a world under climate change. Since their launch in 2015, the 2030 Agenda for Sustainable Development and the Paris Agreement have suffered persistent mismatches, which limit the potential for mutual gains. We use Artificial Intelligence (AI) to assess the degree and type of alignment between the Nationally Determined Contributions (NDCs) and the SDGs. While high income countries tackle the energy-infrastructure-community nexus in term of opportunity, lower income countries make climate impacts more explicit and center their trade-offs around the water-energy-food nexus. These two approaches mark different development trajectories and have non-negligible implications on international financial flow architecture and climate governance. Authors: Francesca Larosa (Royal Institute for Technology); Sergio Hoyas (Universitat Politècnica de València); Fermin Mallor Franco (Royal Institute of Technology); J. Alberto Conejero (Universitat Politècnica de València); Javier García-Martinez (University of Alicante); Francesco Fuso Nerini (Royal Institute of Technology); Ricardo Vinuesa (KTH Royal Institute of Technology)	Natural Language Processing Climate Finance & Economics Societal Adaptation & Resilience Data Mining
NeurIPS 2024	Clustering-Based Framework for Assessing Transportation Resilience to Flood Events (Papers Track) Abstract and authors: (click to expand) Abstract: Flooding presents a significant threat to critical infrastructures (CIs), particularly in Spain, which is frequently cited as the most flood-affected country in Europe. The transportation sector, a crucial CI, is particularly susceptible to such events. It is imperative to monitor the corresponding resilience to improve disaster management efforts. With the advancements in data science and machine learning, various approaches have been developed in this area; however, researchers encounter challenges such as data inconsistency and reliability. This paper presents a resilience assessment framework that utilises machine learning and open data to evaluate the impact of floods on Spain's transportation network. The analysis aims to facilitate well-informed decision-making by stakeholders and government entities, thereby enhancing disaster preparedness and response. Authors: Matheus Pedra (TECNUN); Leire Labaka (TECNUN); Josune Hernantes (TECNUN)	Transportation Cities & Urban Planning Disaster Management and Relief Extreme Weather Time-series Analysis
NeurIPS 2024	Multi-scale decomposition of sea surface height snapshots using machine learning (Papers Track) Abstract and authors: (click to expand) Abstract: Knowledge of ocean circulation is important for understanding and predicting weather and climate, and managing the blue economy. This circulation can be estimated through Sea Surface Height (SSH) observations, but requires decomposing the SSH into contributions from balanced and unbalanced motions (BMs and UBMs). This decomposition is particularly pertinent for the novel SWOT satellite, which measures SSH at an unprecedented spatial resolution. Specifically, the requirement, and the goal of this work, is to decompose instantaneous SSH into BMs and UBMs. While a few studies using deep learning (DL) approaches have shown promise in framing this decomposition as an image-to-image translation task, these models struggle to work well across a wide range of spatial scales and require extensive training data, which is scarce in this domain. These challenges are not unique to our task, and pervade many problems requiring multi-scale fidelity. We show that these challenges can be addressed by using zero-phase component analysis (ZCA) whitening and data augmentation; making this a viable option for SSH decomposition across scales. Authors: Yue Wang (Columbia University); Jingwen Lyu (Columbia University); Chris Pedersen (NYU); Spencer Jones (Texas A&M University); Dhruv Balwada (Columbia University)	Oceans & Marine Systems Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2024	Safe Reinforcement Learning for Remote Microgrid Optimization with Industrial Constraints (Papers Track) Abstract and authors: (click to expand) Abstract: In remote microgrids, the power must be autonomously dispatched between fuel generators, renewable energy sources, and batteries, to fulfill the demand. These decisions must aim to reduce the consumption of fossil fuel and battery degradation while accounting for the complex dynamics of generators, and uncertainty in the demand and renewable production forecasts. Such an optimization could significantly reduce fuel consumption, potentially saving millions of liters of diesel per year. Traditional optimization techniques struggle with scaling in problem complexity and handling uncertainty. On the other hand, reinforcement learning algorithms often lacks the industry constraints guarantees needed for real-world deployment. In this project, we provide a realistic shielded microgrid environment designed to ensure safe control given real-world industry standards. Then, we train a deep reinforcement learning agents to control fuel generators and batteries to minimize the fuel consumption and battery degradation. Our agents outperform heuristics baselines and exhibit a Pareto frontier pattern. Authors: Hadi Nekoei (Mila); Alexandre Blondin Massé (Hydro-Quebec); Rachid Hassani (Hydro-quebec); Sarath Chandar (Mila / École Polytechnique de Montréal); Vincent Mai (Hydro-Québec)	Power & Energy Reinforcement Learning
NeurIPS 2024	CanadaFire2023: Burned Area Mapping Datasets and Benchmarks for Canadian Wildfires in 2023 (Papers Track) Abstract and authors: (click to expand) Abstract: In 2023, wildfires burned record-breaking areas in Canada, resulting in significant carbon loss, exacerbating climate change, and underscoring the need for relevant datasets and machine learning methods for effective and efficient analysis. To understand the fire development processes and assess the climate impact of this natural disaster, burned area mapping datasets are essential for generating high-quality burned scar maps, enabling a comprehensive analysis of the 2023 wildfires, particularly given the vast expanse of Canada. To this end, we propose the CanadaFire2023 dataset, which includes burned area mapping data collected from multiple satellite platforms, namely, Landsat-8, Landsat-9, and Sentinel-2, specifically focused on these wildfires in the recorded history of Canada. To our knowledge, this is the first dataset specifically focused on burned area detection related to the unprecedented 2023 Canadian wildfires, using individual satellite imagery. We also trained four deep learning models—FCN, U-Net, multiscale ResNet, and SegFormer—for burned area mapping and evaluated the mapping performance using binary segmentation metrics, demonstrating that these datasets can serve as benchmarks for the research community studying wildfires and their environmental consequences. The CanadaFire2023 dataset could facilitate downstream applications such as disaster management, carbon emission estimation, and climate change mitigation. Authors: Zilong Zhong (McMaster University); Alemu Gonsamo (McMaster University)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2024	TAUDiff: Improving statistical downscaling for extreme-event simulation using generative diffusion models (Papers Track) Abstract and authors: (click to expand) Abstract: Deterministic regression-based downscaling models for climate variables often suffer from spectral bias, which can be mitigated by generative models like diffusion models. To enable efficient and reliable simulation of extreme weather events, it is crucial to achieve rapid turnaround, dynamical consistency, and accurate spatio-temporal spectral recovery. We propose an efficient correction diffusion model TAUDiff that combines a deterministic spatio-temporal model for mean field downscaling with a smaller generative diffusion model for recovering the fine-scale stochastic features. This approach can not only ensure quicker simulation of extreme events but also reduce overall carbon footprint due to low inference times. Authors: Rahul Sundar (Verisk, India); Nishant Parashar (Verisk); Antoine Blanchard (Verisk); Boyko Dodov (Verisk)	Generative Modeling Extreme Weather
NeurIPS 2024	Emulating the Global Change Analysis Model with Deep Learning (Papers Track) Abstract and authors: (click to expand) Abstract: The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the different pathways to particular outcomes. The interactions and complexity of the coupled human-Earth systems make GCAM simulations costly to run at scale - a requirement for large ensemble experiments which explore uncertainty in model parameters and outputs. A differentiable emulator with similar predictive power, but greater efficiency, could provide novel scenario discovery and analysis of GCAM and its outputs, requiring fewer runs of GCAM. As a first use case, we train a deep learning model on an existing large ensemble that explores a range of GCAM inputs related to different relative contributions of energy production sources, with a focus on wind and solar. We complement this existing ensemble with interpolated input values and a wider selection of outputs, predicting 22,528 GCAM outputs across time, sectors, and regions. We report a median R^2 score of 0.998 for the emulator's predictions and an R^2 score of 0.812 for its input-output sensitivity. Authors: Andrew Holmes (Western Washington University); Matt Jensen (Pacific Northwest National Laboratory); Sarah Coffland (Western Washington University); Hidemi Mitani-Shen (Western Washington University); Logan Sizemore (Western Washington University); Seth Bassetti (Utah State University); Brenna Nieva (Western Washington University); Claudia Tebaldi (Joint Global Change Research Institute); Abigail Snyder (Joint Global Change Research Institute); Brian Hutchinson (Western Washington University)	Climate Science & Modeling Forests Power & Energy
NeurIPS 2024	Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates (Papers Track) Abstract and authors: (click to expand) Abstract: In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset. Authors: Julien Pallage (Polytechnique Montréal, Mila, GERAD); Bertrand Scherrer (Hydro-Québec); Salma Naccache (Hydro-Québec); Christophe Bélanger (Hydro-Québec); Antoine Lesage-Landry (Polytechnique Montréal & GERAD)	Power & Energy Buildings Unsupervised & Semi-Supervised Learning
NeurIPS 2024	Scalable and interpretable deforestation detection in the Amazon rainforest (Papers Track) Abstract and authors: (click to expand) Abstract: Deforestation of the Amazon rainforest is a major contributor to climate change, as it is a crucial precipitation regulator, as well as a large natural carbon reserve. While there have been efforts to create real-time algorithms for deforestation detection, they are oftentimes not accurate or interpretable. We leverage multiple input signals, such as satellite imagery, time-series of deforestation indices and scalar measures, to create a single deep learning model that is both interpretable and accurate. We employ a novel dataset with millions of annotated images of the Brazilian Amazon to train our model, as well as class activation mappings to investigate the added value of interpretability in this context. Authors: Rodrigo Schuller (IMPA); Francisco Ganacim (IMPA); Paulo Orenstein (IMPA)	Interpretable ML Earth Observation & Monitoring Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2024	Operational Wind Speed Forecasts for Chile's Electric Grid Using A Hybrid Machine Learning Model (Papers Track) Abstract and authors: (click to expand) Abstract: As Chile's electric power sector advances toward a future powered by renewable energy, accurate forecasting of renewable generation is essential for managing grid operations. The integration of renewable energy sources is particularly challenging due to the operational difficulties of managing their power generation, which is highly variable compared to fossil fuel sources, delaying the availability of clean energy. To mitigate this, we quantify the impact of increasing intermittent generation from wind and solar on thermal power plants in Chile and introduce a hybrid wind speed forecasting methodology which combines two custom ML models for Chile. The first model is based on TiDE, an MLP-based ML model for short-term forecasts, and the second is based on a graph neural network, GraphCast, for medium-term forecasts up to 10 days. Our hybrid approach outperforms the most accurate operational deterministic systems by 4-21% for short-term forecasts and 5-23% for medium-term forecasts and can directly lower the impact of wind generation on thermal ramping, curtailment, and system-level emissions in Chile. Authors: Dhruv Suri (Stanford University); Praneet Dutta (Google); Flora Xue (Google DeepMind); Ines Azevedo (Stanford University); Ravi Jain (X (formerly Google X))	Climate Science & Modeling Time-series Analysis
NeurIPS 2024	Predicting NOx emissions in Biochar Production Plants using Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: The global Biochar Industry has witnessed a surge in biochar production, with a total of 350k mt/year production in 2023. With the pressing climate goals set and the potential of Biochar Carbon Removal (BCR) as a climate-relevant technology, scaling up the number of new plants to over 1000 facilities per year by 2030 becomes imperative. However, such a massive scale-up presents not only technical challenges but also control and regulation issues, ensuring maximal output of plants while conforming to regulatory requirements. In this paper, we present a novel method of optimizing the process of a biochar plant based on machine learning methods. We show how a standard Random Forest Regressor can be used to model the states of the pyrolysis machine, the physics of which remains highly complex. This model then serves as a surrogate of the machine -- reproducing several key outcomes of the machine -- in a numerical optimization. This, in turn, could enable us to reduce NOx emissions -- a key regulatory goal in that industry -- while achieving maximal output still. In a preliminary test our approach shows remarkable results, proves to be applicable on two different machines from different manufacturers, and can be implemented on standard Internet of Things (IoT) devices more generally. Authors: Marius Köppel (Johannes Gutenberg University Mainz); Tobias Schweitzer (AIRA Holding GmbH); Jochen Weber (None); Erdem Yilmaz (PYREG GmbH); Niklas Witzig (Johannes Gutenberg University Mainz); Tim Klausmann (Johannes Gutenberg University Mainz); Mattia Cerrato (JGU Mainz); Juan Chimbo (ARTi); Bernardo del Campo (ARTi); Lissete Davila (ARTi); David Barreno (ARTi)	Carbon Capture & Sequestration
NeurIPS 2024	Spatio-Temporal Machine Learning Models for Emulation of Global Atmospheric Composition (Papers Track) Abstract and authors: (click to expand) Abstract: Interactive atmospheric composition simulations are among the most computationally expensive components in Earth System Models (ESMs) due to the need to transport a large number of gaseous and aerosol tracers at every model time step. This poses a significant limitation for higher-resolution transient climate simulations with current computational resources. In ESMs such as NASA GISS-E2.1 (hereafter referred to as ModelE), pre-computed monthly-averaged atmospheric composition concentrations are often used to reduce computational expenses. This approach is referred to as Non-Interactive Tracers (NINT). In this study, we extend the NINT version of the ModelE using machine learning to emulate the effects of interactive emissions on climate forcing. We use data from a fully interactive composition climate model with surface-driven emissions to develop an ML-based NINT climate model. This version accounts for instantaneous atmospheric conditions, enabling the tracers to respond dynamically to meteorology without the need for explicit calculation of tracer transport. This approach could be applied to any aerosol species and integrated into ESMs to simulate aerosol concentrations interactively. The proposed framework emulates the advection term at the surface pressure level, with a focus on predicting surface-level concentrations of Black Carbon (BC) from biomass burning, which is a contributor to elevated levels of PM2.5 concentrations. Two consecutive years of ModelE simulated data were used as training data. To capture both temporal and spatial dependencies, a Convolutional Long Short-Term Memory (ConvLSTM) model was used. Results show the ConvLSTM achieved an average R-squared of 0.85 (STD = 0.08) on the test set. In contrast, using monthly-averaged atmospheric composition concentrations resulted in an average R-squared of 0.42 (STD = 0.73) for the same period. Authors: Mohammad Erfani (Columbia University); Kara Lamb (Columbia University); Susanne Bauer (NASA Goddard Institute for Space Studies); Kostas Tsigaridis (Columbia University); Marcus van Lier-Walqui (Columbia University); Gavin Schmidt (NASA Goddard Institute for Space Studies)	Climate Science & Modeling Time-series Analysis
NeurIPS 2024	Probabilistic representation learning of subseasonal to seasonal ocean dynamics (Papers Track) Abstract and authors: (click to expand) Abstract: Data-driven weather and climate forecasts on subseasonal-to-seasonal (S2S) scales face a two-fold challenge: the observational records are comparatively short and predictability strongly depends on the coupling of earth systems evolving on very different timescales. As a first step towards general subseasonal-to-seasonal predictions, we model the El Niño Southern Oscillation (ENSO), one of the principal sources of predictability on S2S scales. Characterised by anomalously warm sea surface temperatures in the tropical Pacific, an El Niño event arises from a combination of slow heat transfer within the Pacific and between ocean basins, as well as fast atmospheric dynamics, such as westerly wind bursts and convection. Here, we design a deep learning model that can flexibly represent information from modalities on different timescales, trained as a Masked Autoencoder and optimising the Empirical Continuous Ranked Probability Score. We find that the representation learning approach exhibits zero-shot performance that is competitive with task-specific models on S2S of ENSO in terms of correlation skill. To the best of our knowledge, we are the first to predict well-calibrated uncertainty estimates on a 24 month horizon. Authors: Jannik Thuemmel (University of Tuebingen); Jakob Schlör (ECMWF); Florian Ebmeier (University of Tuebingen); Bedartha Goswami (University of Tübingen)	Unsupervised & Semi-Supervised Learning Climate Science & Modeling
NeurIPS 2024	Estimating atmospheric variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models (Papers Track) Abstract and authors: (click to expand) Abstract: This study explores the application of diffusion models in the field of typhoons, predicting multiple ERA5 meteorological variables simultaneously from Digital Typhoon satellite images. The focus of this study is taken to be Taiwan, an area very vulnerable to typhoons. By comparing the performance of Conditional Denoising Diffusion Probability Model (CDDPM) with Convolutional Neural Networks (CNN) and Squeeze-and-Excitation Networks (SENet), results suggest that the CDDPM performs best in generating accurate and realistic meteorological data. Specifically, CDDPM achieved a PSNR of 32.807, which is approximately 7.9% higher than CNN and 5.5% higher than SENet. Furthermore, CDDPM recorded an RMSE of 0.032, showing a 11.1% improvement over CNN and 8.6% improvement over SENet. A key application of this research can be for imputation purposes in missing meteorological datasets and generate additional high-quality meteorological data using satellite images. It is hoped that the results of this analysis will enable more robust and detailed forecasting, reducing the impact of severe weather events on vulnerable regions Authors: Zhangyue Ling (Imperial College London); Pritthijit Nath (University Of Cambridge); Cesar Quilodran-Casas (Imperial College London)	Computer Vision & Remote Sensing Extreme Weather Generative Modeling
NeurIPS 2024	RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors (Papers Track) Abstract and authors: (click to expand) Abstract: Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both. This paper introduces a physics-informed machine learning-based framework to enhance grid's resiliency. Specifically, when encountering disruptive events, this paper designs remedial control actions to prevent blackouts. The proposed~\textbf{P}hysics-\textbf{G}uided \textbf{R}einforcement \textbf{L}earning (PG-RL) framework determines effective real-time remedial line-switching actions, considering their impact on power balance, system security, and grid reliability. To identify an effective blackout mitigation policy, PG-RL leverages power-flow sensitivity factors to guide the RL exploration during agent training. Comprehensive evaluations using the Grid2Op platform demonstrate that incorporating physical signals into RL significantly improves resource utilization within electric grids and achieves better blackout mitigation policies -- both of which are critical in addressing climate change. Authors: Anmol Dwivedi (RPI); Ali Tajer (RPI); Santiago Paternain (Rensselaer Polytechnic Institute); Nurali Virani (GE Research)	Power & Energy Hybrid Physical Models Reinforcement Learning
NeurIPS 2024	ATLAS: A spend classification benchmark for estimating scope 3 carbon emissions (Papers Track) Abstract and authors: (click to expand) Abstract: The majority (70%) of companies reporting their value chain emissions rely on financial spend ledger and emissions factors per dollar. Accurate classification of expenditures to emissions factors is critical but complex, given the sheer number of line items and the diversity of how they are categorized and described. This is an area where Large Language Models (LLMs) can play a key role. However, there is currently no benchmark dataset to evaluate the performance of LLM-based solutions. Here, we introduce the Aggregate Transaction Ledgers for Accounting Sustainability dataset or, ATLAS, and the initial evaluation results of four models using ATLAS. ATLAS is the first spend classification benchmark and is comprised of 10,000 synthetic, labeled spend items reflecting the distribution of corporate expenditures. We evaluate four baseline models, with the best model achieving a top-1 accuracy of 57.3% and a top-3 accuracy of 72.2%. ATLAS enables systematic evaluation of LLMs for spend classification. Our results provide a starting point for advancing automated carbon accounting and sustainability reporting for spend- based emissions. Authors: Andrew Dumit (Watershed Technology, Inc.); Krishna Rao (Watershed Technology, Inc.); Travis Kwee (Watershed Technology, Inc.); Varsha Gopalakrishnan (Watershed Technology Inc.); Katherine Tsai (Watershed Technology, Inc.); Sangwon Suh (Watershed Technology, Inc.)	Natural Language Processing
NeurIPS 2024	Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures (Papers Track) Abstract and authors: (click to expand) Abstract: Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarchical nature of benthic organisms covering reef substrata, i.e., coral taxonomic levels and health condition. To address this limitation, we propose to annotate benthic images using hierarchical classification. Experiments on a custom dataset from a Northeast Brazilian coral reef show that our approach outperforms flat classifiers, improving both F1 and hierarchical F1 scores by approximately 2\% across varying amounts of training data. In addition, this hierarchical method aligns more closely with ecological objectives. Authors: Celia Blondin (IRD); Joris Guerin (IRD, Univ. Montpellier); Laure Berti-Equille (IRD); Guilherme Ortigara Longo (Universidade Federal do Rio Grande do Norte); Kelly Inagaki (Universidade Federal do Rio Grande do Norte)	Oceans & Marine Systems
NeurIPS 2024	Optimizing NMR Spectroscopy Pulse Sequencing with Reinforcement Learning for Soil Atomic Abundance (Papers Track) Abstract and authors: (click to expand) Abstract: Determining the amount of sequestered carbon in soils and monitoring soil health in farmlands is an important climate change problem. Motivated by the lack of scalable and inexpensive techniques for in-situ soil health monitoring, we focus on low-voltage nuclear magnetic resonance (NMR) spectroscopy as a promising new approach and develop a reinforcement learning technique to modulate NMR pulses for rapid atomic abundance assessment of soils. Our preliminary results derived using Monte Carlo sampling and parallelized OpenAI Gym training show the promise of our RL-based approach. Authors: Rohan Shenoy (UC Berkeley); Hans Gaensbauer (MIT); Elsa Olivetti (MIT); Evan Coleman (MIT)	Agriculture & Food Carbon Capture & Sequestration Reinforcement Learning
NeurIPS 2024	Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction (Papers Track) Overall Best Paper Abstract and authors: (click to expand) Abstract: Understanding spatio-temporal patterns in polar ice layers is essential for tracking changes in ice sheet balance and assessing ice dynamics. While convolutional neural networks are widely used in learning ice layer patterns from raw echogram images captured by airborne snow radar sensors, noise in the echogram images prevents researchers from getting high-quality results. Instead, we focus on geometric deep learning using graph neural networks, aiming to build a spatio-temporal graph neural network that learns from thickness information of the top ice layers and predicts for deeper layers. In this paper, we developed a novel multi-branch spatio-temporal graph neural network that used the GraphSAGE framework for spatio features learning and a temporal convolution operation to capture temporal changes, enabling different branches of the network to be more specialized and focusing on a single learning task. We found that our proposed multi-branch network can consistently outperform the current fused spatio-temporal graph neural network in both accuracy and efficiency. Authors: Zesheng Liu (Lehigh University); Maryam Rahnemoonfar (Lehigh University)	Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring
NeurIPS 2024	Resolution-Agnostic Transformer-based Climate Downscaling (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding future weather changes at regional and local scales is crucial for planning and decision-making, particularly in the context of extreme weather events, as well as for broader applications in agriculture, insurance, and infrastructure development. However, the computational cost of downscaling Global Climate Models (GCMs) to the fine resolutions needed for such applications presents a significant barrier. Drawing on advancements in weather forecasting models, this study introduces a cost-efficient downscaling method using a pretrained Earth Vision Transformer (Earth ViT) model. Initially trained on ERA5 data to downscale from 50 km to 25 km resolution, the model is then tested on the higher resolution BARRA-SY dataset at 3 km resolutions. Remarkably, it performs well without additional training, demonstrating its ability to generalize across different resolutions. This approach holds promise for generating large ensembles of regional climate simulations by downscaling GCMs with varying input resolutions without incurring additional training costs. Ultimately, this method could provide more comprehensive estimates of potential future changes in key climate variables, aiding in effective planning for extreme weather events and climate change adaptation strategies. Authors: Declan Curran (UNSW); Hira Saleem (UNSW); Sanaa Hobeichi (The University of New South Wales); Flora Salim (University of New South Wales)	Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2024	Scalable Satellite Imagery Analysis: A Cascade Framework for Sparse Target Detection (Papers Track) Abstract and authors: (click to expand) Abstract: Remote sensing is a crucial tool for monitoring events affecting climate change, such as tracking forest loss, identifying pollution sources, and monitoring the deployment of renewable energy infrastructure. However, applying state-of-the-art deep learning models to monitor the entire Earth is expensive. In this paper, we propose a cascade framework to reduce this cost: we apply a small MLP on precomputed embeddings of each image patch to serve as a preliminary filter, identifying key patches that warrant further examination by more resource-intensive deep models. Our approach reduces per-task inference runtime by 5x with a <1% impact on accuracy. By reducing inference cost, our method enables nonprofits and other organizations with limited resources to scale monitoring efforts to more environmental and conservation applications. Authors: Arvind Manivannan (University of Washington); Tarun Narayanan Venkatachalam (Allen Institute for AI); Yanlin Huang (University of Washington); Favyen Bastani (Allen Institute for AI)	Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2024	Estimating Greenland Ice Sheet Dynamics Using Neural Operators (Papers Track) Abstract and authors: (click to expand) Abstract: This study investigates the application of neural operators, particularly the Fourier Neural Operator (FNO), to enhance the modeling accuracy of the Greenland ice sheet's internal layers. Traditional computational methods often struggle to capture the complex spatial patterns of ice sheets adequately. Our approach leverages a radar image dataset to evaluate the effectiveness of neural operators in comparison to traditional neural network models. The FNO outperformed other models in key performance metrics, such as pixel error and layer accuracy. The results highlight the potential of neural operators to significantly improve the computational efficiency and accuracy of environmental models, paving the way for their broader application in cryospheric science and climate change research. Authors: Maryam Rahnemoonfar (Lehigh University); Heling Wang (Lehigh University)	Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2024	Enhanced Detection of Human-Driven Forest Alterations using Echo State Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Forest monitoring is crucial for understanding ecosystem dynamics, detecting changes, and implementing effective conservation strategies. In this work, we propose a novel approach for automated detection of human-induced changes in woodlands using Echo State Networks (ESNs) and satellite imagery. The utilization of ESNs offers a promising solution for analyzing time-series data and identifying deviations indicative of forest alterations, particularly those caused by human activities such as deforestation and logging. The proposed experimental setup leverages satellite imagery to capture temporal variations in the Normalized Difference Vegetation Index (NDVI), and involves the training and evaluation of ESN models using extensive datasets from Chile's central region, encompassing diverse woodland environments and human-induced disturbances. Our initial experiments demonstrate the effectiveness of ESNs in predicting NDVI values and detecting deviations indicative of human-related changes in woodlands, even in the presence of climate-induced changes like drought and browning. Our work contributes to forest monitoring by offering a scalable and efficient solution for automated change detection in woodland environments. Integrating ESNs with satellite imagery analysis provides valuable insights into human impacts on forest ecosystems, facilitating informed decision-making for sustainable land management and biodiversity conservation. Authors: Tomás Couso (PUC); Paula Aguirre (PUC); Rodrigo Carrasco (PUC); Javier Lopatin (UAI)	Time-series Analysis
NeurIPS 2024	Optimizing Carbon Emissions and Cost Reductions for Household Energy Demand Using Machine Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Residential buildings account for 17-20% of global greenhouse gas (GHG) emissions, posing a significant challenge in transitioning all buildings to net zero. While heat pumps and building retrofits can significantly reduce emissions, homeowners are often unaware of their energy usage and the potential for cost reduction. Supervised machine learning methods have the potential to provide actionable insights for individual energy end users, enabling them to reduce both GHG emissions and energy costs. We propose a novel framework that utilizes baseline, intervention, and optimization models to predict emissions and cost estimates for individual energy end users. This paper presents a novel application of an optimization model for energy bills through machine learning methods: (1) classification of time series data for electricity and gas usage baselines, (2) prediction of GHG emission reductions, and (3) prediction of energy cost reductions. This study suggests energy retrofit policy implications using machine learning as an enabling technology for empowering decision-makers and end-users to tackle climate change. Authors: Shivani Chotalia (Clean AI Initiative); Kyungmin Lee (University of Delaware); Soazig Kaam (N/A); Victor Hutse (Radix); David Quispe (University of Toronto)	Buildings Time-series Analysis
NeurIPS 2024	Machine Learning Models for Predicting Solar Power Potential and Energy Efficiency in some underserved localities of the Congo Basin Region (Proposals Track) Abstract and authors: (click to expand) Abstract: This research proposal aims to develop machine learning models—specifically Linear Regression, Long Short-Term Memory, and Convolutional Neural Networks—to accurately predict solar power potential and energy efficiency in selected underserved localities within the Congo Basin. The Congo Basin, recognized for its ecological significance as the world's second-largest tropical rainforest, faces severe energy access challenges, especially in rural communities that rely on traditional biomass for heating and cooking. This dependency intensifies deforestation and contributes to climate change through increased greenhouse gas emissions. Despite substantial solar energy potential of the region, access to clean and renewable energy sources remains limited. The study will compare the models based on accuracy, reliability, training times, and memory usage, generating actionable insights for development agencies and local stakeholders. By enabling informed, data-driven decisions regarding sustainable energy solutions, this work intends to facilitate a transition from traditional biomass to renewable energy sources, ultimately contributing to both environmental conservation and improved quality of life for local populations. Authors: Jean de Dieu NGUIMFACK NDONGMO (The University of Bamenda); Adelaide Nicole KENGNOU TELEM (University of Buea); Reeves MELI FOKENG (The University of Bamenda)	Power & Energy
NeurIPS 2024	A Hybrid Machine Learning Model For Ship Speed ThroughWater: Solve And Predict (Proposals Track) Abstract and authors: (click to expand) Abstract: This research proposes a hybrid model for predicting ship speed through water, addressing challenges in estimating GHG emissions from shipping while contributing to climate change mitigation. Predicting ship speed through water is a key element in calculating GHG emissions. However, few models address this prediction in a way that integrates both physical principles and machine learning. Our approach combines physical modeling with data-driven techniques to predict real ship speed through water in two key steps: "Solve" and "Predict". In the first step “Solve”, a differential equation is resolved to estimate speed through calm water. "Predict" step uses a machine learning model that incorporates maritime and meteorological conditions and historical data to improve speed predictions in real-world conditions. This hybrid approach leverages both physics-based knowledge and machine learning models to provide a more comprehensive solution for accurately predicting ship speed through water. Authors: Zakarya ELMIMOUNI (ENSAE); Ayoub Atanane (UQAR); Loubna Benabbou (UQAR)	Oceans & Marine Systems Hybrid Physical Models
NeurIPS 2024	Enhanced Cruise Control for Reducing Automobile Environmental Impact (Proposals Track) Abstract and authors: (click to expand) Abstract: Automobiles are one of the main contributors to climate change. We propose an enhanced Advanced Cruise Control (ACC) system that leverages Reinforcement Learning (RL) using location and terrain data along with onboard sensors and cameras to provide better driving characteristics and improve fuel economy. Authors: Shubhankar Gahlot (University of Canterbury NZ)	Transportation Reinforcement Learning
NeurIPS 2024	Exploring Climate Awareness and Anxiety in Teens: An Expert-Driven AI Perspective (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate awareness and climate anxiety often go hand in hand.The growing awareness of climate change among young people is increasingly shadowed by climate anxiety, a condition marked by profound stress, fear, and a sense of helplessness stemming from overwhelming information on environmental crises and perceived inaction by authorities. Our proposal is an innovative approach using Large Language Model (LLM)-based chatbots to support children and adolescents in fostering climate awareness, managing climate anxiety, and promoting sustainable practices. By collaborating with schools and engaging a multidisciplinary experts and the young people themselves, we seek to co-create an impactful educational intervention. Starting with a comprehensive survey of school counsellors to map the current state of climate awareness and anxiety, and to understand their expectations for our AI solution, this project is poised for global scalability, addressing the pressing mental health challenges associated with climate change, particularly in vulnerable and resource-constrained regions. Authors: Sruthi Viswanathan (University of Oxford and The Spaceship Academy); Omar Mohammed (Independant Researcher); Craig Vezina (The Spaceship Academy); Mia Doces (Commitee For Children)	Societal Adaptation & Resilience Health Interpretable ML
NeurIPS 2024	Generating Climate Dataset in a Data-scarce Region of Choke Mountain Watersheds in Ethiopia Using Machine Learning Techniques (Proposals Track) Abstract and authors: (click to expand) Abstract: In regions where climate data is scarce, adapting to climate change becomes a significant challenge due to the lack of reliable information. This project addresses this issue by using Artificial Intelligence (AI) techniques to generate comprehensive climate datasets in a data-scarce region of Choke Mountain Watersheds in Ethiopia. The primary objectives are to fill gaps in existing in-situ precipitation and temperature observations and to create data for areas that are currently unmonitored. By applying advanced machine learning algorithms, we will improve the accuracy and reliability of climate data, and fill gaps in current datasets to ensure completeness. Ensuring the availability of a continuous dataset is crucial for informed decision-making in climate change adaptation. Authors: Sintayehu Abebe (Debre Markos University); Kassahun Tadesse (Debre Markos University); Mulu Kerebih (Debre Markos University); Bekalu Asres (Debre Markos University); Bewketu Mulu (Debre Markos University); Varsha Gopalakrishnan (Self)	Earth Observation & Monitoring Extreme Weather Time-series Analysis
NeurIPS 2024	Mamba MethaneMapper: State Space Model for Methane Detection from Hyperspectral Imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: Methane (CH4) is the chief contributor to global climate change. Recent advancements in AI-based image processing have paved the way for innovative approaches for the detection of methane using hyper-spectral imagery. Existing methods, while effective, often come with high computational demands and associated costs that can limit their practical applications. Addressing these limitations, we propose the Mamba MethaneMapper (MMM), a cost-effective and efficient AI-driven solution designed to enhance methane detection capabilities in hyper-spectral images. MMM will incorporate two key innovations that collectively improve performance while managing costs. First, we will utilize a gpu-aware state-space encoder, which optimizes the computational resources and efficiency of the system. Second, MMM will use an environment-sensitive module to prioritize image regions likely containing methane emissions, which are then analyzed by our efficient Mamba algorithm. This selective approach not only improves the accuracy of methane detection but also significantly reduces unnecessary computations and memory consumption. Authors: Satish Kumar (University of California, Santa Barbara); ASM Iftekhar (Microsoft); Kaikai Liu (University of California, Santa Barbara); Bowen Zhang (University Of California, Santa Barbara); Mehan Jayasuriya (Mozilla)	Climate Science & Modeling Earth Observation & Monitoring Computer Vision & Remote Sensing Meta- and Transfer Learning
NeurIPS 2024	Unlocking the Potential of Green Virtual Bidding Strategies : A Pathway to a Low-Carbon Electricity Market (Proposals Track) Abstract and authors: (click to expand) Abstract: The increasing importance of renewable energy in mitigating climate change has led to a critical examination of electricity market mechanisms that can support this transition. Virtual bidding, a financial tool used within electricity markets, allows market participants to capitalize on discrepancies between the Day-Ahead (DA) and Real-Time (RT) prices of electricity. The introduction of virtual bidding within electricity markets has introduced significant changes in market dynamics, with implications for environmental outcomes. It supports the transition towards a greener energy mix by favoring the dispatch of renewable resources and contributing to more efficient market conditions. This proposal seeks to explore the impact of virtual bidding and the development of green virtual bidding strategies by leveraging advanced machine learning models. Authors: Aya Laajil (Centrale Supelec); Loubna Benabou (UQAR); Frédérique M. Gagnon (Videns Analytics); Laurent Barcelo (Videns Analytics); Ghait Boukachab (Videns Analytics & Mila)	Climate Finance & Economics Power & Energy Time-series Analysis
NeurIPS 2024	Cross-Border Electricity Price Forecasting with Deep Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Accurate electricity price forecasting (EPF) is critical for the efficient operation of energy markets, especially with the increasing integration of renewable energy sources. In this study, we explore the performance of advanced deep learning models, including Long Short-Term Memory (LSTM), vanilla Transformers, Adaptive Fourier Neural Operator (AFNO), and Mamba, in forecasting electricity prices across 16 bidding zones in the European Union. By utilizing a comprehensive dataset that includes cross-border electricity prices and generation data, we compare the effectiveness of these models under different learning strategies, including zero-shot, one-shot, and few-shot learning. We hope our results set a new benchmark for future EPF studies and offer valuable insights into the dynamics of electricity pricing in energy markets. Authors: Hadeer El Ashhab (KIT); Benjamin Schäfer (Karlsruhe Institute of Technology)	Power & Energy Time-series Analysis
NeurIPS 2024	Making Climate AI Systems Past and Future Aware to Better Evaluate Climate Change Policies (Proposals Track) Abstract and authors: (click to expand) Abstract: Addressing the issues faced by climate change necessitates appropriate methodologies for evaluating climate policies, particularly when discussing long-term and real-world scenarios. While large language models (LLMs) have altered artificial intelligence, they ultimately fall short of connecting historical data with future estimates. We propose an agentic LLM system that would address this gap by considering and analyzing the probable outcomes of the user-specified climate policy inside the practical settings. Further, we propose using knowledge graphs to model the existing data about the impact of climate policies along with allowing our system to access the data about future climate predictions. Done this way, the model can peek into the past (previous policies) and the future (climate scenarios forecast), paving the way for agencies to evaluate and design strategies and plans for climate change more effectively. Authors: Riya . (IIT Roorkee); Sudhakar Singh (Nvidia)	Natural Language Processing Climate Finance & Economics Earth Observation & Monitoring Public Policy
NeurIPS 2024	A Novel Approach to Assessing the Efficacy of Policy-driven Urban Energy Demand Reduction via Proximal Infrared Remote Sensing (Proposals Track) Abstract and authors: (click to expand) Abstract: Byproducts of energy use are among the leading drivers of emissions that can lead to climate change. Heating and cooling represent the largest source of energy use in urban infrastructure; thus, significant policy efforts have been implemented to reduce energy demand. Estimates of the efficacy of these policies are often difficult as they rely on self-reporting or detailed utility data, which is not available broadly and challenging to collect in areas where utility infrastructures are unavailable. Buildings' heating, ventilation, and air conditioning (HVAC) can be used to measure energy consumption from the demand side to evaluate end use and potential energy policy-induced reductions. Therefore, we are proposing a novel, non-intrusive method of energy use monitoring that uses proximal infrared remote sensing of building envelopes to find patterns of heating and cooling use. Our results show that the technique can discern on-and-off patterns of externally facing cooling units (ACs) in a dense urban scene at distances up to ~0.5 miles. We have tested multiple cases of residential buildings in New York City. We collected ~337,000 infrared images at 10-second intervals of the buildings’ facades taken from June 11 to July 18, 2018, between 500 m and 1 km. We have applied computer vision techniques to that sequence of images to identify exterior-venting HVAC units and generate their infrared time series, which correlates directly with their temperature. We determine each AC unit's on/off transitions using a one-dimensional edge detection algorithm and identify aggregated and disaggregated patterns of end-user behavior that are direct proxies for total heating and cooling use. Finally, we demonstrate that this new methodology is sufficiently sensitive to assess policy interventions to reduce daily peak demand effectively. Authors: Kyungmin Lee (University of Delaware); Gregory Dobler (University of Delaware)	Buildings Computer Vision & Remote Sensing
NeurIPS 2024	Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate change's destruction of marine biodiversity is threatening communities and economies around the world which rely on healthy oceans for their livelihoods. The challenge of applying computer vision to niche, real-world domains such as ocean conservation lies in the dynamic and diverse environments where traditional top-down learning struggle with long-tailed distributions, generalization, and domain transfer. Scalable species identification for ocean monitoring is particularly difficult due to the need to adapt models to new environments and identify rare or unseen species. To overcome these limitations, we propose leveraging bottom-up, open-domain learning frameworks–specifically vision-language models (VLMs) combined with retrieval-augmented generation (RAG)–as a resilient, scalable solution for image and video analysis in marine applications. We validate this approach through a preliminary application in classifying fish from video onboard fishing vessels, demonstrating impressive emergent retrieval and prediction capabilities without domain-specific training or knowledge of the task itself. Authors: Sepand Dyanatkar (OnDeck Fisheries AI); Angran Li (OnDeck Fisheries AI); Alexander Dungate (OnDeck Fisheries AI)	Computer Vision & Remote Sensing Earth Observation & Monitoring Ecosystems & Biodiversity Unsupervised & Semi-Supervised Learning
NeurIPS 2024	How are companies reducing emissions? An LLM-based approach to creating a carbon emissions reduction levers library at scale (Proposals Track) Abstract and authors: (click to expand) Abstract: Creating a transparent, sector-specific database of actions that would result in carbon emissions reduction is essential for guiding companies toward effective, data-driven pathways to meet their net-zero commitments. Information on carbon emissions reduction levers is scattered around greenhouse gas emissions disclosures and sustainability reports in dense text forms, and no systematic, sector and region specific reduction lever libraries are available to companies. This research proposes a multi-agent system leveraging Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) to systematically extract, classify, and validate carbon reduction actions from publicly available sustainability reports. By constructing a standardized database of reduction levers categorized by industry, geography, and greenhouse gas scopes, this work empowers companies to prioritize high-impact, cost-effective emissions reduction strategies. We plan to integrate environmentally-extended input-output models to ensure that these actions are closely tied to sector-specific emissive sources, increasing their relevance and scalability. This initiative is expected to support companies in mitigating greenhouse gas emissions by offering a practical resource that accelerates the transition to a low-carbon economy, and makes actionable insights readily available to corporations, industry and the research community. Authors: Varsha Gopalakrishnan (Watershed Technology Inc.); Shaena Ulissi (Watershed); Andrew Dumit (Watershed Technology, Inc.); Krishna Rao (Watershed Technology Inc.); Katherine Tsai (Watershed Technology Inc.); Sangwon Suh (Watershed Technology Inc.)	Natural Language Processing
NeurIPS 2024	Enhanced PINNs for high-order power grid dynamics (Proposals Track) Abstract and authors: (click to expand) Abstract: We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We propose some novel enhancements to improve PINN training and accuracy and also implement several other recently proposed ideas from the literature. We successfully apply these to study the transient dynamics of synchronous generators. We also make progress towards applying PINNs to advanced inverter models. Such enhanced PINNs can allow us to accelerate high-fidelity simulations needed to ensure a stable and reliable renewables-rich future grid. Authors: Vineet Jagadeesan Nair (MIT)	Power & Energy Hybrid Physical Models Unsupervised & Semi-Supervised Learning
NeurIPS 2024	Multimodal AI framework for predicting candidate high temperature superconductors (Proposals Track) Abstract and authors: (click to expand) Abstract: Materials science is at the forefront of addressing some of the most pressing challenges of our era, particularly in enhancing energy efficiency and sustainability. One of the most promising avenues in this field is the study of superconductors—materials that, when cooled below a critical temperature (Tc), exhibit zero electrical resistance. This unique property not only eliminates energy loss due to resistance but also enables a wide range of advanced technologies, such as MRI machines, magnetically levitating trains, and other high-efficiency systems. Superconductors can significantly reduce the carbon footprint of power transmission and other industrial applications. Given the complexity and importance of predicting candidate and practical high-temperature superconductors, we propose to develop a multimodal AI framework to predict new high-Tc superconducting materials. By integrating various material properties, including structural and compositional data, we seek to study patterns and relationships that could guide the discovery of new high-temperature superconductors. Success in this endeavor could significantly reduce energy losses in electrical systems, contributing to the fight against climate change. Authors: Nidhish Sagar (Massachusetts Institute of Technology); Eslam G. Al-Sakkari (Polytechnique Montréal); Ahmed Ragab (Polytechnique Montréal)	Chemistry & Materials Power & Energy Generative Modeling Meta- and Transfer Learning
NeurIPS 2024	Seeing Inside Buildings: Leveraging Generative AI and Multimodal Data to Automate Building Material Audits (Proposals Track) Abstract and authors: (click to expand) Abstract: Building retrofits and deconstruction projects are expected to surpass new construction jobs globally, putting pressure on architects and developers to sustainably reuse buildings and their materials (World Economic Forum). However, building reuse and material upcycling is hindered by the complexity of building material audits, which require expensive tests, site visits, and detailed data that is often missing for old structures. We present a Generative AI approach to predict the structural and material make-up of existing buildings from multimodal geospatial, technical, and cadaster data. Leveraging a dataset of 100 buildings across the United States with corresponding building 3D scans, geolocation, and construction data, we demonstrate the capability of a stable diffusion model to reliably predict structural diagrams for subsequent estimation of material contents. This process also offers designers actionable potential material reuse data to streamline and accelerate circularity for existing building design. Authors: Nikita Klimenko (MIT); James Stoddart (Autodesk, Inc.); Lorenzo Villaggi (Autodesk, Inc.); Dale Zhao (Autodesk, Inc.)	Buildings Carbon Capture & Sequestration Computer Vision & Remote Sensing Generative Modeling
NeurIPS 2024	Flood Prediction in Kenya - Leveraging Pre-Trained Models to Generate More Validation Data in Sparse Observation Settings (Proposals Track) Abstract and authors: (click to expand) Abstract: Kenya has lacked a national flood risk management framework and also has sparse flood observation data, which makes developing deep learning flood prediction models on a national scale challenging. Flood prediction models are critical to operationalize Early Warning Systems (EWS). We propose two different models to feed into an EWS. The first model will leverage statistical machine learning approaches to predict flood or no flood events on a 0.25 x 0.25 degree scale (approximately 30 km x 30 km in Kenya) using ERA5 features as well as land cover and Digital Terrain Model (DTM) data. This first model will also be used to create a baseline prediction benchmark across the entire country of Kenya. The second model will leverage pre-trained remote sensing based models to generate segmented flood or no flood data on a fine spatial scale. This will increase the number of validation points by a factor of 1000, which opens the door to deep learning approaches to predict flood or no-flood events on a 30 meter x 30 meter spatial scale. We hope that this approach of leveraging pre-trained models to generate fine scale validation data can eventually be used widely in other extreme climate event forecasting scenarios given the scarcity of historical extreme climate events compared to normal weather events. Authors: Alim Karimi (Self / Purdue University); David Quispe (University of Toronto); Hammed Akande (Concordia University); Nicole Mong'are (Athi Water Works Development Agency); Valerie Brosnan (Mitga Solutions); Asbina Baral (Ministry of Education, Science and Technology)	Societal Adaptation & Resilience Climate Science & Modeling Disaster Management and Relief Earth Observation & Monitoring Extreme Weather Computer Vision & Remote Sensing Time-series Analysis
NeurIPS 2024	Towards more efficient agricultural practices via transformer-based crop type classification (Proposals Track) Abstract and authors: (click to expand) Abstract: Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to several downstream policy and research applications. This proposal presents preliminary work showing that it is possible to accurately classify crops from time series derived from Sentinel 1 and 2 satellite imagery in Mexico using a pixel-based binary crop/non-crop time series transformer model. We also find preliminary evidence that meta-learning approaches supplemented with data from similar agro-ecological zones may improve model performance. Due to these promising results, we propose further development of this method with the goal of accurate multi-class crop classification in Jalisco, Mexico via meta-learning with a dataset comprising similar agro-ecological zones. Authors: Isabella Smythe (Columbia University); Eduardo Ulises Moya (Gobierno de Jalisco); Michael Smith (Aspia Space); Yazid Mikail (Climate Change AI); Daisy Ondwari (Kabarak University)	Agriculture & Food Earth Observation & Monitoring Computer Vision & Remote Sensing Meta- and Transfer Learning Time-series Analysis
NeurIPS 2024	A Multimodal Causal Framework for Large-Scale Ecosystem Valuation: Application to Wetland Benefits for Flood Mitigation (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate change is poised to alter wetland ecosystems through changes in temperature and precipitation patterns, compounding the already pronounced influence of human-driven wetland development. In this context, policymakers and environmental managers would benefit from accurate wetland valuations to guide their decision-making, as their choices regarding this critical natural resource directly impact flood mitigation efforts, biodiversity conservation, and economic activity. This paper introduces a novel multimodal causal framework for producing location-specific ecosystem valuations at a national scale to be used in cost-benefit policy analysis. It leverages recent advances in estimating heterogeneous treatment effects to flexibly determine how the expected impact of ecosystem-level changes---such as wetland loss via development---varies conditional on high-dimensional and multimodal measures that characterize the complex interactions between human and natural systems such as aerial satellite imagery, weather sequence data, land cover classifications, and water surface networks. From this effort, we aim to create a national database of location-specific wetland valuations in an approach that can be readily extended in estimating the effect of other interventions on ecosystems. We also plan to generate open-source feature embeddings for each U.S. wetland, embeddings that can be used to address other climate-related causal questions as well. Authors: Hannah Druckenmiller (Caltech); Georgia Gkioxari (Caltech); Connor Jerzak (University of Texas at Austin); SayedMorteza Malaekeh (University of Texas at Austin)	Ecosystems & Biodiversity Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2024	Stubble (Crop Residue) Burning Detection Through Satellite Images Using Geospatial Foundation Model: A Case Study in Punjab, India (Proposals Track) Abstract and authors: (click to expand) Abstract: Stubble burning is a significant environmental challenge globally, with widespread implications for air quality, greenhouse gases emission, soil degradation and health issues. This practice is particularly prevalent in agricultural regions across the world, though its impacts are notably severe in the northern India. This proposed work focuses on improving the detection of stubble (crop residue) burning in Punjab (India), through using the geospatial foundation model. This study leverages a series of satellite images where stubble burning incidents have been documented. By refining the model to incorporate local environmental factors, this study aims to improve the accuracy of stubble burning detection, thereby contributing to a scalable solution for real-time monitoring and intervention in crop residue burning practices worldwide. Authors: Rajiv Ranjan (Plaksha University); Ying-Jung Chen (Georgia Institute of Technology); Shashank Tamaskar (Plaksha University); Anupam Sobti (IIT Delhi)	Computer Vision & Remote Sensing Agriculture & Food
NeurIPS 2024	Leveraging Artificial Intelligence for Real-Time Carbon Emission Management and Climate Change Resilience Planning in Kenya (Proposals Track) Abstract and authors: (click to expand) Abstract: As climate change intensifies, reducing carbon emissions has emerged as a critical global objective. Countries like Kenya, which heavily depend on agriculture and pastoral systems, are disproportionately affected by changing climatic conditions. In 2020, Kenya’s Nationally Determined Contribution (NDC) set a target to reduce carbon emissions by 32% by 2030. However, traditional methods for tracking and managing carbon emissions are derailed by inconsistencies, such as data manipulation and lack of real-time monitoring. This project leverages blockchain technology to provide a transparent and reliable platform for tracking CO2 emissions. Additionally, remote sensing and machine learning models will be used to enhance data collection, monitoring, and predicting emissions. The study will be piloted in 16 sugar companies and 16 plastic companies, as they are among the highest carbon emitters in Kenya. The findings from this study will assist the government and industries in planning climate change resilience strategies and adaptation measures. Authors: Alukwe Jones (Kabarak University); Theophilus Owiti (Kabarak University); Mercy Gachoka (Kabarak Univeristy); Peter Rogendo (Kabarak University)	Carbon Capture & Sequestration Time-series Analysis Unsupervised & Semi-Supervised Learning
NeurIPS 2024	DeepMyco - Dataset Generation for Dye Mycoremediation (Proposals Track) Abstract and authors: (click to expand) Abstract: Textile dyes comprise 20% of global water pollution. Mycoremediation, a promising approach utilizing cheap, naturally growing fungi, has not seen scale production. While numerous studies indicate benefits, it is challenging to apply the specific learnings of each study to the combination of environmental factors present in a given physical site - a gap we believe machine learning can help fill if datasets become available. We propose an approach to drive machine learning research in mycoremediation by contributing a comprehensive dataset. We propose using advanced language models and vision transformers to extract and categorize experimental data from various research papers. This dataset will enable ML-driven innovation in matching fungi to specific dye types, optimizing remediation processes, and scaling up mycoremediation efforts effectively. Authors: Danika Gupta (The Harker Upper School)	Heavy Industry and Manufacturing Natural Language Processing
NeurIPS 2024	Large language model co-pilot for transparent and trusted life cycle assessment comparisons (Proposals Track) Abstract and authors: (click to expand) Abstract: Intercomparing life cycle assessments (LCA), a common type of sustainability and climate model, is difficult due to basic differences in fundamental assumptions, especially in the goal and scope definition stage. This complicates decision-making and the selection of climate-smart policies, as it becomes difficult to compare optimal products and processes between different studies. To aid policymakers and LCA practitioners alike, we plan to leverage large language models (LLM) to build a database containing documented assumptions for LCAs across the agricultural sector, with a case study on livestock management. The articles for this database are identified in a systematic literature search, then processed to extract relevant assumptions about the goal and scope definition of the LCA and inserted into a vector database. We then leverage this database to develop an AI co-pilot by augmenting LLMs with retrieval augmented generation to be used by stakeholders and LCA practitioners alike. This co-pilot will accrue two major benefits: 1) enhance the decision-making process through facilitating comparisons among LCAs to enable policymakers to adopt data-driven climate policies and 2) encourage the use of common assumptions by LCA practitioners. Ultimately, we hope to create a foundational model for LCA tasks that can plug-in with existing open source LCA software and tools. Authors: Nathan Preuss (Cornell University); Fengqi You (Cornell University)	Natural Language Processing Agriculture & Food Public Policy Supply Chains Data Mining Generative Modeling
ICLR 2024	Mapping Land Naturalness from Sentinel-2 using Deep Contextual and Geographical Priors (Papers Track) Abstract and authors: (click to expand) Abstract: In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which humans alter their surroundings. As our actions continue to impact natural areas, using satellite images to see and measure these effects has become crucial for understanding and fighting climate change. Aiming to map land naturalness on the continuum of modern human pressure, we develop a multi-modal supervised deep learning framework that addresses the unique challenges of satellite data and the task at hand. We incorporate contextual and geographical priors. These priors are represented by corresponding coordinate information and broader contextual information including and surrounding the immediate patch to be predicted. Our framework improves the model's predictive performance to map land naturalness from a given Sentinel-2 data, a multi-spectral optical satellite imagery. Recognizing that our protective measures are as effective as our grasp of the ecosystem, quantifying naturalness serves as a crucial step towards enhancing our environmental stewardship. Authors: Burak Ekim (University of the Bundeswehr); Michael Schmitt (University of the Bundeswehr Munich)	Ecosystems & Biodiversity Forests
ICLR 2024	Structured spectral reconstruction for scalable soil organic carbon inference (Papers Track) Abstract and authors: (click to expand) Abstract: Measuring soil organic carbon (SOC) inexpensively and accurately is crucial for soil health monitoring and agricultural decarbonization. Hyperspectral imaging is commonly evaluated as an inexpensive alternative to dry combustion for SOC measurement, but existing end-to-end approaches trained to predict SOC content from spectral data frequently fail to generalize when applied outside of their ground-truth geographic sampling distributions. Using stratified data from the USDA Rapid Carbon Assessment (RaCA), we demonstrate a method to improve model generalization out-of-distribution by training SOC regression alongside models that reconstruct input spectra. Because hyperspectra can be collected from remote platforms such as drones and satellites, this approach raises the possibility of using large hyperspectral Earth observation datasets to transfer SOC inference models to remote geographies where geographically-dense ground-truth data collection may be expensive or impossible. By replacing the decoder with a simple physics-informed model, we also learn an interpretable spectral signature of SOC, confirming its dark hue and expected reflectance troughs. Finally, we show that catastrophic generalization failures can be better addressed with these architectures by fine-tuning on large quantities of hyperspectral data. Authors: Evan A Coleman (MIT); Sujay Nair (Georgia Institute of Technology); Xinyi Zeng (Coho Climate Advisors); Elsa Olivetti (MIT Department of Materials Science & Engineering)	Earth Observation & Monitoring Computer Vision & Remote Sensing
ICLR 2024	ClimateQ&A : bridging the gap between climate scientists and the general public (Papers Track) Abstract and authors: (click to expand) Abstract: This research paper investigates public views on climate change and biodiversity loss by analyzing questions asked to the ClimateQ&A platform. ClimateQ&A is a conversational agent that uses LLMs to respond to queries based on over 14,000 pages of scientific literature from the IPCC and IPBES reports. Launched online in March 2023, the tool has gathered over 30,000 questions, mainly from a French audience. Its chatbot interface allows for the free formulation of questions related to nature*. While its main goal is to make nature science more accessible, it also allows for the collection and analysis of questions and their themes. Unlike traditional surveys involving closed questions, this novel method offers a fresh perspective on individual interrogations about nature. Running NLP clustering algorithms on a sample of 3,425 questions, we find that a significant 25.8% inquire about how climate change and biodiversity loss will affect them personally (e.g., where they live or vacation, their consumption habits) and the specific impacts of their actions on nature (e.g., transportation or food choices). This suggests that traditional methods of surveying may not identify all existing knowledge gaps, and that relying solely on IPCC and IPBES reports may not address all individual inquiries about climate and biodiversity, potentially affecting public understanding and action on these issues. *Note: we use “nature” as an umbrella term for “climate change” and “biodiversity loss”. Authors: Natalia de la Calzada (Ekimetrics); Theo Alves Da Costa (Ekimetrics); Annabelle Blangero (Ekimetrics); Nicolas CHESNEAU (EKIMETRICS)	Natural Language Processing Behavioral and Social Science
ICLR 2024	Improving Streamflow Predictions with Vision Transformers (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global Long Short-Term Memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-ViT-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a vision transformer. Applied to 531 basins across the United States (US), our method significantly outperforms existing models, showing an 11% increase in prediction accuracy. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for managing water resources under climate change. Authors: Kshitij Tayal (Oak Ridge National Lab); Arvind Renganathan (University of Minnesota); Dan Lu (Oak Ridge National Laboratory)	Oceans & Marine Systems Time-series Analysis
ICLR 2024	Grapevine Disease Prediction Using Climate Variables from Multi-Sensor Remote Sensing Imagery via a Transformer Model (Papers Track) Abstract and authors: (click to expand) Abstract: Early detection and management of grapevine diseases are important in pursuing sustainable viticulture. This paper introduces a novel framework leveraging the TabPFN model to forecast blockwise grapevine diseases using climate variables from multi-sensor remote sensing imagery. By integrating advanced machine learning techniques with detailed environmental data, our approach significantly enhances the accuracy and efficiency of disease prediction in vineyards. The TabPFN model's experimental evaluations showcase comparable performance to traditional gradient-boosted decision trees, such as XGBoost, CatBoost, and LightGBM. The model's capability to process complex data and provide per-pixel disease-affecting probabilities enables precise, targeted interventions, contributing to more sustainable disease management practices. Our findings underscore the transformative potential of combining Transformer models with remote sensing data in precision agriculture, offering a scalable solution for improving crop health and productivity while reducing environmental impact. Authors: Weiying Zhao (Deep Planet); Natalia Efremova (Queen Mary University London)	Agriculture & Food Earth Observation & Monitoring Causal & Bayesian Methods
ICLR 2024	Towards Ecological Network Analysis with Gromov-Wasserstein Distances (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is driving the widespread redistribution of species with cascading effects on predators and their prey. Formally comparing ecological interaction networks is a critical step towards understanding the impact of climate change on ecosystem functioning, yet current methods for ecological network analysis are unable to do so. We propose using the GromovWasserstein (GW) metric for quantifying dissimilarity between ecological networks. We demonstrates the corresponding optimal transport plans of this distance can be interpreted as species functional alignment between food webs. Our results show that GW transport plans align species from different mammal communities consistent with ecological understanding. Furthermore, we illustrate extensions of the GW distance to notions of averages and factorization over ecological networks. Ultimately, we propose the foundation for a novel interpretable topological data analysis framework to inform future ecological research and conservation management. Authors: Kai M Hung (Rice University); Ann Finneran (Rice University); Alex Zalles (Rice University); Lydia Beaudrot (Rice University); Cesar Uribe (Rice University)	Ecosystems & Biodiversity
ICLR 2024	Scaling Transformers for Skillful and Reliable Medium-range Weather Forecasting (Papers Track) Overall Best Paper Abstract and authors: (click to expand) Abstract: Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success. Here we introduce Stormer, a simple transformer model that achieves state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer’s favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens. Authors: Tung Nguyen (University of California, Los Angeles); Rohan Shah (Carnegie Mellon University); Hritik Bansal (UCLA); Troy Arcomano (Argonne National Laboratory); Sandeep Madireddy (Argonne National Laboratory); Romit Maulik (Argonne National Laboratory); Veerabhadra Kotamarthi (Argonne National Laboratory); Ian Foster (Computation Institute); Aditya Grover (UCLA)	Climate Science & Modeling Extreme Weather
ICLR 2024	Categorization of Meteorological Data by Contrastive Clustering (Papers Track) Abstract and authors: (click to expand) Abstract: Visualized ceilometer backscattering data, displaying meteorological phenomena like clouds, precipitation, and aerosols, is mostly analyzed manually by meteorology experts. In this work, we present an approach for the categorization of backscattering data using a contrastive clustering approach, incorporating image and spatiotemporal information into the model. We show that our approach leads to meteorologically meaningful clusters, opening the door to the automatic categorization of ceilometer data, and how our work could potentially create insights in the field of climate science. Authors: Michael Dammann (HAW Hamburg); Ina Mattis (DWD); Michael Neitzke (HAW Hamburg); Ralf Möller (University of Lübeck)	Unsupervised & Semi-Supervised Learning Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2024	AI-driven emulation of ocean dynamics on sub-seasonal scales (Papers Track) Abstract and authors: (click to expand) Abstract: Climate forecasting systems rely on coupling atmospheric models to ocean and sea ice models. However, while there have recently been significant efforts to accelerate atmospheric models using AI, there have been very scarce efforts to accelerate the latter. As a result, climate forecasting systems still rely on expensive numerical simulations, which renders large-scale ensembling and probabilistic prediction cumbersome. To address this issue, we propose a large-scale AI model of ocean dynamics. Our method relies on a spherical neural operator to accurately capture the functional nature of ocean dynamics on the sphere. We empirically demonstrate that our model can accurately predict ocean dynamics for sub-seasonal horizons and outperforms the existing method. It offers a 60x speedup over the fastest numerical solver currently used for the task. Authors: Suyash Bire (Massachusetts Institute of Technology); Jean Kossaifi (NVIDIA); Simone Silvestri (Massachusetts Institute of Technology); Nikola Kovachki (Nvidia Corp.); Kamyar Azizzadenesheli (Nvidia Corp.); Chris N Hill (MIT); Animashree Anandkumar (Caltech)	Climate Science & Modeling Generative Modeling
ICLR 2024	Calibrating Bayesian UNet++ for Sub-seasonal Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big impact on the world. Calibration of the neural networks provides a way to ensure our confidence in the predictions. However, calibrating regression models is an under-researched topic, especially in forecasters. We calibrate a UNet++ based architecture, which was shown to outperform physics-based models in temperature anomalies. We show that with a slight trade-off between prediction error and calibration error, it is possible to get more reliable and sharper forecasts. We believe that calibration should be an important part of safety-critical machine learning applications such as weather forecasters. Authors: Büşra Asan (Istanbul Technical University); Abdullah Akgül (University of Southern Denmark); Alper Unal (Istanbul Technical University); Melih Kandemir (University of Southern Denmark); Gozde Unal (Istanbul Technical University)	Uncertainty Quantification & Robustness Climate Science & Modeling
ICLR 2024	Extreme Precipitation Nowcasting using Transformer-based generative models (Papers Track) Abstract and authors: (click to expand) Abstract: This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, specifically VideoGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed VideoGPT-EVL in generating accurate precipitation forecasts, especially when dealing with extreme precipitation events. Authors: Cristian Meo (TUDelft); Mircea T Lica (Delft University of Technology); Ankush Roy (TUDelft); Zeina Boucher (TUDelft); Junzhe Yin (TUDelft); Yanbo Wang (Delft University of Technology); Ruben Imhoff (Deltares); Remko Uijlenhoet (TUDelft); Justin Dauwels (TU Delft)	Generative Modeling Climate Science & Modeling Computer Vision & Remote Sensing Time-series Analysis
ICLR 2024	GeoFormer: A Vision and Sequence Transformer-based Approach for Greenhouse Gas Monitoring (Papers Track) Abstract and authors: (click to expand) Abstract: Air pollution represents a pivotal environmental challenge globally, playing a major role in climate change via greenhouse gas emissions and negatively affecting the health of billions. However, predicting the spatial and temporal patterns of pollutants remains challenging. The scarcity of ground-based monitoring facilities and the dependency of air pollution modeling on comprehensive datasets, often inaccessible for numerous areas, complicate this issue. In this work, we introduce GeoFormer, a compact model that combines a vision transformer module with a highly efficient time-series transformer module to predict surface-level nitrogen dioxide (NO2) concentrations from Sentinel-5P satellite imagery. We train the proposed model to predict surface-level NO2 measurements using a dataset we constructed with Sentinel-5P images of ground-level monitoring stations, and their corresponding NO2concentration readings. The proposed model attains high accuracy (MAE 5.65), demonstrating the efficacy of combining vision and time-series transformer architectures to harness satellite-derived data for enhanced GHG emission insights, proving instrumental in advancing climate change monitoring and emission regulation efforts globally. Authors: Madhav Khirwar (Independent)	Computer Vision & Remote Sensing Climate Science & Modeling Time-series Analysis
ICLR 2024	Paddy Doctor: Open Dataset and Automated Pest Identification Using Pre-trained Deep Learning Models (Papers Track) Abstract and authors: (click to expand) Abstract: Agriculture is one of the most important industries contributing to several countries’ national income. Timely identification of these pests is crucial for both farmers and agriculture experts to implement effective mitigation methods. Traditionally, farmers employ manual techniques based on their experience and visual inspection to identify paddy pests, but this is highly inefficient, time-consuming, and error-prone. At times, even experienced farmers and agriculture experts might struggle to identify a specific species of pests due to their size, colors similar to leaves, and the extensive variety of identical types. Moreover, farmers often apply a large quantity of pesticide without accurately identifying the exact type of pests and their underlying causes. Therefore, it is increasingly important to automate the process of detection of paddy pests to reduce pesticide usage and subsequently minimize the loss in yield. In this paper, we present an open dataset and deep learning models for automated pest identification in real paddy fields. Our dataset contains 6,062 annotated paddy leaf images across 17 classes (16 pest classes and a normal class). We benchmarked our dataset using six pre-trained models (ResNet34, VGG16, DenseNet121, EfficientNet_v2_m, MobileNet_V3_Large, and SqueezeNet1_0). The experimental results showed that ResNet34 achieved the highest accuracy of 98.31%. We release our dataset and reproducible code in the open source for community use. Authors: Petchiammal A (Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu); Pandarasamy Arjunan (Indian Institute of Science, Bangalore)	Agriculture & Food
ICLR 2024	Using expired weather forecasts to supply 10 000y of data for accurate planning of a renewable European energy system (Papers Track) Abstract and authors: (click to expand) Abstract: Expanding renewable energy generation and electrifying heating to address climate change will heighten the exposure of our power systems to the variability of weather. Planning and assessing these future systems typically lean on past weather data. We spotlight the pitfalls of this approach---chiefly its reliance on what we claim is a limited weather record---and propose a novel approach: to evaluate these systems on two orders of magnitude more weather scenarios. By repurposing past ensemble weather predictions, we not only drastically expand the known weather distribution---notably its extreme tails---for traditional power system modeling but also unveil its potential to enable data-intensive self-supervised, diffusion-based and optimization ML techniques. Building on our methodology, we introduce a **dataset** collected from ECMWF ENS forecasts, encompassing power-system relevant variables over Europe, and detail the intricate process behind its assembly. Authors: Petr Dolezal (AI4ER CDT, University of Cambridge); Emily Shuckburgh (University of Cambridge)	Power & Energy Climate Science & Modeling Data Mining Uncertainty Quantification & Robustness
ICLR 2024	Black carbon plumes from gas flaring in North Africa identified from multi-spectral imagery with deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: Black carbon (BC) is an important pollutant aerosol emitted by numerous human activities, including gas flaring. Improper combustion in flaring activities can release large amounts of BC, which is harmful to human health and has a strong climate warming effect. To our knowledge, no study has ever directly monitored BC emissions from satellite imagery. Previous works quantified BC emissions indirectly, by applying emission coefficients to flaring volumes estimated from satellite imagery. Here, we develop a deep learning framework and apply it to Sentinel-2 imagery over North Africa during 2022 to detect and quantify BC emissions from gas flaring. We find that BC emissions in this region amount to about 1 million tCO2eq, or 1 million passenger cars, more than a quarter of which are due to 10 sites alone. This work demonstrates the operational monitoring of BC emissions from flaring, a key step in implementing effective mitigation policies to reduce the climate impact of oil and gas operations. Authors: Alexandre Tuel (Galeio); Thomas Kerdreux (INRIA/ ENS); Louis THIRY (ENS Paris)	Earth Observation & Monitoring Computer Vision & Remote Sensing
ICLR 2024	Time-Varying Constraint-Aware Reinforcement Learning for Energy Storage Control (Papers Track) Abstract and authors: (click to expand) Abstract: Energy storage devices, such as batteries, thermal energy storages, and hydrogen systems, can help mitigate climate change by ensuring a more stable and sustainable power supply. To maximize the effectiveness of such energy storage, determining the appropriate charging and discharging amounts for each time period is crucial. Reinforcement learning is preferred over traditional optimization for the control of energy storage due to its ability to adapt to dynamic and complex environments. However, the continuous nature of charging and discharging levels in energy storage poses limitations for discrete reinforcement learning, and time-varying feasible charge-discharge range based on state of charge (SoC) variability also limits the conventional continuous reinforcement learning. In this paper, we propose a continuous reinforcement learning approach that takes into account the time-varying feasible charge-discharge range. An additional objective function was introduced for learning the feasible action range for each time period, supplementing the objectives of training the actor for policy learning and the critic for value learning. This actively promotes the utilization of energy storage by preventing them from getting stuck in suboptimal states, such as continuous full charging or discharging. This is achieved through the enforcement of the charging and discharging levels into the feasible action range. The experimental results demonstrated that the proposed method further maximized the effectiveness of energy storage by actively enhancing its utilization. Authors: Jaeik Jeong (Electronics and Telecommunications Research Institute (ETRI)); Tai-Yeon Ku (Electronics and Telecommunications Research Institute (ETRI)); Wan-Ki Park (Electronics and Telecommunications Research Institute (ETRI))	Power & Energy Reinforcement Learning Time-series Analysis
ICLR 2024	Towards a Data-Driven Understanding of Cloud Structure Formation (Papers Track) Abstract and authors: (click to expand) Abstract: The physics of cloud formation and evolution is still not fully understood and constitutes one of the highest uncertainties in climate modeling. We are working on an approach that aims at improving our understanding of how clouds of different structures form from a data-driven perspective: By predicting the visual appearance of cloud photographs from physical quantities obtained from reanalysis data and subsequently attributing the decisions to physical quantities using ``explainable AI'' methods, we try to identify relevant physical processes. At the current stage, this is just a proof of concept, being at least able to identify basic meteorologically plausible facts from data. Authors: Ann-Christin Wörl (Johannes Gutenberg University); Michael Wand (University of Mainz); Peter Spichtinger (Johannes Gutenberg University)	Interpretable ML Climate Science & Modeling
ICLR 2024	Bee Activity Prediction and Pattern Recognition in Environmental Data (Papers Track) Abstract and authors: (click to expand) Abstract: As a consequence of climate change, biodiversity is declining rapidly. Many species like insects, especially bees, suffer from changes in temperature and rainfall patterns. Applying machine learning for monitoring and predicting specie's health and life conditions can help understanding and improving biodiversity. In this work we use data collected from cameras and sensors mounted upon beehives together with different other data sources like weather data, information extracted from satellite images and geographical information. We aim at predicting bees' health (measured as their activity) and analyzing influencing environmental conditions. We show that we are able to accurately predict bees' activity and understand their life conditions by using machine learning algorithms and explainable AI. Understanding these conditions can help to make recommendations on good locations for beehives. This work illustrates the potential of applying machine learning on sensor, satellite and weather data for monitoring and predicting species' health and hence shows the ability for adaptation to climate change and a more accurate species monitoring. Authors: Christine Preisach (University of Applied Sciences Karlsruhe); Marius Herrmann (Karlsruhe Institute of Technology)	Ecosystems & Biodiversity Interpretable ML
ICLR 2024	Advancing Earth System Model Calibration: A Diffusion-Based Method (Papers Track) Honorable Mention Abstract and authors: (click to expand) Abstract: Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses. Authors: Yanfang Liu (Oak Ridge National Laboratory); Dan Lu (Oak Ridge National Laboratory); Zezhong Zhang (Oak Ridge National Laboratory); Feng Bao (Florida State University); Guannan Zhang (Oak Ridge National Laboratory)	Generative Modeling Ecosystems & Biodiversity Uncertainty Quantification & Robustness
ICLR 2024	Towards Downscaling Global AOD with Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Poor air quality represents a significant threat to human health, especially in urban areas. To improve forecasts of air pollutant mass concentrations, there is a need for high-resolution Aerosol Optical Depth (AOD) forecasts as proxy. However, current General Circulation Model (GCM) forecasts of AOD suffer from limited spatial resolution, making it difficult to accurately represent the substantial variability exhibited by AOD at the local scale. To address this, a deep residual convolutional neural network (ResNet) is evaluated for the GCM to local scale downscaling of low-resolution global AOD retrievals, outperforming a non-trainable interpolation baseline. We explore the bias correction potential of our ResNet using global reanalysis data, evaluating it against in-situ AOD observations. The improved resolution from our ResNet can assist in the study of local AOD variations. Authors: Josh Millar (Imperial College London); Paula Harder (Mila); Lilli J Freischem (University of Oxford); Philipp Weiss (University of Oxford); Philip Stier (University of Oxford)	Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2024	Reconstructing the Breathless Ocean with Spatio-Temporal Graph Learning (Papers Track) Abstract and authors: (click to expand) Abstract: The ocean is currently undergoing severe deoxygenation. Accurately reconstructing the breathless ocean is crucial for assessing and protecting marine ecosystem in response to climate change. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first spatio-temporal graph learning model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the ocean deoxygenation in data-driven manner. Authors: Bin Lu (Shanghai Jiao Tong University); Ze Zhao (Shanghai Jiao Tong University); Luyu Han (Shanghai Jiao Tong University); Xiaoying Gan (Shanghai Jiao Tong University); Yuntao Zhou (Shanghai Jiao Tong University); Lei Zhou (Shanghai Jiao Tong Univ); Luoyi Fu (Shanghai Jiao Tong University); Xinbing Wang (Shanghai Jiao Tong University); Chenghu Zhou (Institute of Geographic Sciences and Natural Resources Research, CAS); Jing Zhang (Shanghai Jiao Tong University)	Oceans & Marine Systems Data Mining Time-series Analysis
ICLR 2024	Analyzing the secondary wastewater-treatment process using Faster R-CNN and YOLOv5 object detection algorithms (Papers Track) Abstract and authors: (click to expand) Abstract: The activated sludge (AS) process is the most common type of secondary wastewater treatment, applied worldwide. Due to the complexity of microbial communities, imbalances between the different types of bacteria may occur and disturb the process, with pronounced economical and environmental consequences. Microscopic inspection of the morphology of flocs and microorganisms provides key information on AS properties and function. This is a time-consuming, highly skilled, and expensive process that is not readily available in all locations. Thus, most wastewater-treatment plants do not carry out this essential analysis, resulting in frequent operational faults. In this study, we develop a novel deep learning (DL) object detection algorithm to analyze and monitor the AS process based on a unique microscopic image database of flocs and microorganisms. Specifically, we applied YOLOv5 and Faster R-CNN algorithms as tools for segmentation and object detection to analyze the wastewater. The mean average precision (mAP) of the YOLOv5 was 0.67, outperforming the Faster R-CNN by 15%. Histogram equalization preprocessing of both bright-field and phase-contrast images significantly improved the results of the algorithm in all classes. In the case of YOLOv5, the mAP increased by 16.67%, to 0.77, where the AP of protozoa, filaments, and open floc classes outperformed the previous model by over 20%. These results demonstrate the potential of leveraging DL algorithms to enhance the analysis and monitoring of WWTPs in an affordable manner, consequently reducing environmental pollution caused by contaminated effluent. The fundamental challenge addressed herein has important global relevance, especially in an era in which the demand for high-quality wastewater reuse is expected to increase dramatically. Authors: Offir Inbar (Tel-Aviv University); Moni Shahar (Tel Aviv University); Jacob Gidron (Tel-Aviv University); Ido Cohen (Tel-Aviv University); Dror Avisar (Tel-Aviv University)	Agriculture & Food Cities & Urban Planning Ecosystems & Biodiversity Health Chemistry & Materials Computer Vision & Remote Sensing
ICLR 2024	Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models (Papers Track) Abstract and authors: (click to expand) Abstract: Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale. Generative deep learning approaches are suitable for emulating these tools due to their computational efficiency and ability, once trained, to generate realizations in a fraction of the time required by ESMs. We extend previous work that used a generative probabilistic diffusion model to emulate ESMs by targeting the joint emulation of multiple variables, temperature and precipitation, by a single diffusion model. Joint generation of multiple variables is critical to generate realistic samples of phenomena resulting from the interplay of multiple variables. The diffusion model emulator takes in the monthly mean-map of temperature and precipitation and produces the daily values of each of these variables that exhibit statistical properties similar to those generated by ESMs. Our results show the outputs from our extended model closely resemble those from ESMs on various climate metrics including dry spells and hot streaks, and that the joint distribution of temperature and precipitation in our sample closely matches those of ESMs. Authors: Katie Christensen (Western Washington University); Lyric Otto (Western Washington University); Seth Bassetti (Utah State University); Claudia Tebaldi (Joint Global Change Research Institute); Brian Hutchinson (Western Washington University)	Climate Science & Modeling Generative Modeling
ICLR 2024	Verifying Practices of Regenerative Agriculture: African Smallholder Farmer Dataset for Remote Sensing and Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Despite Africa’s contribution to global greenhouse gas (GHG) emissions being only a few %, the continent experiences the harshest impacts, particularly within its food production systems. Regenerative agriculture is receiving a large amount of attention as a method to strengthen both food security and climate change resilience in Africa. For practicing regenerative agriculture, carbon credits are issued, but verifying the methodologies on a large scale is one of the challenging points in popularizing it. In this paper, we provide a comprehensive dataset on regenerative agriculture in sub-Saharan Africa. The dataset has field polygon information and is labeled with several types of regenerative agriculture methodologies. The dataset can be applied to local site analysis, classification, and detection of regenerative agriculture with remote sensing and machine learning. We also highlight several machine learning models and summarize the baseline results on our dataset. We believe that by providing this dataset, we can contribute to the establishment of verification methods for regenerative agriculture. The dataset can be downloaded from https://osf.io/xgp9m/. Authors: Yohei Nakayama (Degas Ltd); Grace Antwi (Degas Ltd); Seiko Shirasaka (Keio University)	Agriculture & Food Computer Vision & Remote Sensing
ICLR 2024	Semi-Supervised Domain Adaptation for Wildfire Detection (Papers Track) Abstract and authors: (click to expand) Abstract: Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompasses 30 times more diverse labeled scenes than the current largest benchmark wildfire dataset, HPWREN, and introduces a new labeling policy for wildfire detection. Inspired by coordconv., we propose a robust baseline, Location-Aware Object Detection for Semi-Supervised Domain Adaptation (LADA), utilizing a teacher-student based self-supervised Domain Adaptation framework capable of extracting translational variance features characteristic of wildfires. Our framework significantly outperforms the existing baseline by a notable margin of 3.8\%p in mean Average Precision on the HPWREN wildfire dataset. Authors: Joo Young Jang (Alchera); Youngseo Cha (Alchera); Jisu Kim (Alchera); SooHyung Lee (Alchera); Geonu Lee (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera); Nojun Kwak (Seoul National University)	Unsupervised & Semi-Supervised Learning Disaster Management and Relief Computer Vision & Remote Sensing
ICLR 2024	Identifying Climate Targets in National Laws and Policies using Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Quantified policy targets are a fundamental element of climate policy, typically characterised by domain-specific and technical language. Current methods for curating comprehensive views of global climate policy targets entail significant manual effort. At present there are few scalable methods for extracting climate targets from national laws or policies, which limits policymakers’ and researchers’ ability to (1) assess private and public sector alignment with global goals and (2) inform policy decisions. In this paper we present an approach for extracting mentions of climate targets from national laws and policies. We create an expert-annotated dataset identifying three categories of target (’Net Zero’, ’Reduction’ and ’Other’ (e.g. renewable energy targets)) and train a classifier to reliably identify them in text. We investigate bias and equity impacts related to our model and identify specific years and country names as problematic features. We explore the dataset generated from applying our classifier to the Climate Policy Radar (CPR) dataset, showcasing the potential for automated data collection and research support in climate policy. Our work represents a significant upgrade in the accessibility of these key climate policy elements for policymakers and researchers. Authors: Matyas Juhasz (Climate Policy Radar); Tina Marchand (Climate Policy Radar); Roshan Melwani (Climate Policy Radar); Kalyan Dutia (Climate Policy Radar); Sarah Goodenough (Climate Policy Radar); Harrison Pim (Climate Policy Radar); Henry Franks (Climate Policy Radar)	Public Policy
ICLR 2024	Empowering Sustainable Finance: Leveraging Large Language Models for Climate-Aware Investments (Papers Track) Abstract and authors: (click to expand) Abstract: With the escalating urgency of climate change, it is becoming more imperative for businesses and organizations to align their objectives with sustainability goals. Financial institutions also face a critical mandate to fulfill the Sustainable Development Goals (SDGs), particularly goal 13, which targets the fight against climate change and its consequences. Mitigating the impacts of climate change requires a focus on reducing supply chain emissions, which constitute over 90% of total emission inventories. In the financial industry, supply chain emissions linked to lending and investments emerge as the primary source of emissions, posing challenges in tracking financed emissions due to the intricate process of collecting data from numerous suppliers across the supply chain. To address these challenges, we propose an emission estimation framework utilizing a Large Language Model (LLM) to drastically accelerate the assessment of the emissions associated with lending and investment activities. This framework utilizes financial activities as a proxy for measuring financed emissions. Utilizing the LLM, we classify financial activities into seven asset classes following the Partnership for Carbon Accounting Financials (PCAF) standard. Additionally, we map investments to industry categories and employ spend-based emission factors (kg-CO2/$-spend) to calculate emissions associated with financial investments. In our study, we compare the performance of our proposed method with state-of-the-art text classification models like TF-IDF, word2Vec, and Zero-shot learning. The results demonstrate that the LLM-based approach not only surpasses traditional text mining techniques and performs on par with a subject matter expert (SME) but most importantly accelerates the assessment process. Authors: Ayush Jain (IBM Research); Manikandan Padmanaban (IBM Research India); Jagabondhu Hazra (IBM Research India); Shantanu Godbole (IBM India); Hendrik Hamann (IBM Research)	Natural Language Processing Supply Chains
ICLR 2024	Near-real-time monitoring of global ocean carbon sink (Papers Track) Abstract and authors: (click to expand) Abstract: The ocean, absorbing about 25% of anthropogenic CO2 emissions, plays a crucial role in mitigating climate change. However, the delayed (by one year) traditional estimates of ocean-atmosphere CO2 flux hinder timely understanding and response to the global carbon cycle’s dynamics. Addressing this challenge, we introduce Carbon Monitor Ocean (CMO-NRT), a pioneering dataset providing near-real-time, monthly gridded estimates of global surface ocean fugacity of CO2 (fCO2) and ocean-atmosphere CO2 flux from January 2022 to July 2023. This dataset marks a significant advancement by updating the global carbon budget’s estimates through a fusion of data from 10 Global Ocean Biogeochemical Models (GOBMs) and 8 data products into a near-real-time analysis framework. By harnessing the power of Convolutional Neural Networks (CNNs) and semi-supervised learning techniques, we decode the complex nonlinear relationships between model or product estimates and observed environmental predictors. The predictive models, both for GOBM and data products, exhibit exceptional accuracy, with root mean square errors (RMSEs) maintaining below the 5% threshold. This advancement supports more effective climate change mitigation efforts by providing scientists and policymakers with timely and accurate data. Authors: Xiaofan Gui (Microsoft Research); Jiang Bian (Microsoft Research)	Climate Science & Modeling Unsupervised & Semi-Supervised Learning
ICLR 2024	WindDragon: enhancing wind power forecasting with automated deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) wind power forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power. Authors: Julie Keisler (INRIA, EDF R&D); Etienne Le Naour (Sorbonne University, EDF R&D)	Power & Energy
ICLR 2024	Neural Processes for Short-Term Forecasting of Weather Attributes (Papers Track) Abstract and authors: (click to expand) Abstract: Traditional weather prediction models rely on solving complex physical equations, with long computation time. Machine learning models can process large amount of data more quickly. We propose to use neural processes (NPs) for short-term weather attributes forecasting. This is a novel avenue of research, as previous work has focused on NPs for long-term forecasting. We compare a multi-task neural process (MTNP) to an ensemble of independent single-task NPs (STNP) and to an ensemble of Gaussian processes (GPs). We use time series data for multiple weather attributes from Chichester Harbour over a one-week period. We evaluate performance in terms of NLL and MSE with 2-hours and 6-hours time horizons. When limited context information is provided, the MTNP leverages inter-task knowledge and outperforms the STNP. The STNP outperforms both the MTNP and the GPs ensemble when a sufficient, but not exceeding, amount of context information is provided. Authors: Benedetta L Mussati (University of Oxford); Helen McKay (Mind Foundry); Stephen Roberts (University of Oxford)	Time-series Analysis Meta- and Transfer Learning
ICLR 2024	EU Climate Change News Index: Forecasting EU ETS prices with online news (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon emission allowance prices have been rapidly increasing in the EU since 2018 and accurate forecasting of EU Emissions Trading System (ETS) prices has become essential. This paper proposes a novel method to generate alternative predictors for daily ETS price returns using relevant online news information. We devise the EU Climate Change News Index by calculating the term frequency–inverse document frequency (TF–IDF) feature for climate change-related keywords. The index is capable of tracking the ongoing debate about climate change in the EU. Finally, we show that incorporating the index in a simple predictive model significantly improves forecasts of ETS price returns. Authors: Aron Pap (BGSE); Aron D Hartvig (Corvinus University of Budapest, Cambridge Econometrics); Péter Pálos (Budapest University of Technology and Economics)	Climate Finance & Economics Power & Energy Public Policy Natural Language Processing Time-series Analysis
ICLR 2024	Explaining Zeolite Synthesis-Structure Relationships using Aggregated SHAP Analysis (Papers Track) Abstract and authors: (click to expand) Abstract: Zeolites, crystalline aluminosilicate materials with well-defined porous structures, have emerged as versatile materials with applications in carbon capture. Hydrothermal synthesis is a widely used method for zeolite production, offering control over crystallinity and and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding to optimize the synthesis process. We train a supervised classification machine learning model on ZeoSyn (a dataset of zeolite synthesis routes) to predict the zeolite framework product given a synthesis route. Subsequently, we leverage SHapley Additive Explanations (SHAP) to reveal key synthesis-structure relationships in zeolites. To that end, we introduce an aggregation SHAP approach to extend such analysis to explain the formation of composite building units (CBUs) of zeolites. Analysis at this unprecedented scale sheds light on key synthesis parameters driving zeolite crystallization. Authors: Elton Pan (MIT)	Chemistry & Materials Carbon Capture & Sequestration Interpretable ML
ICLR 2024	Fast non-stationary geospatial modelling with multiresolution (wavelet) Gaussian processes (Papers Track) Abstract and authors: (click to expand) Abstract: Climate modelling tasks involve assimilating large amounts of geospatial data from different sources, such as simulators and measurements from weather stations and satellites. These sources of data are weighted according to their uncertainty, so good quality uncertainty estimates are essential. Gaussian processes (GPs) offer flexible models with uncertainty estimates, and have a long track record of use in geospatial modelling. However, much of the research effort, including recent work on scalability, is focused on statistically stationary models, which are not suitable for many climatic variables, such as precipitation. Here we propose a novel, scalable, nonstationary GP model based upon discrete wavelets, and evaluate them on toy and real world data. Authors: Talay M Cheema (University of Cambridge); Carl Edward Rasmussen (Cambridge University)	Causal & Bayesian Methods Climate Science & Modeling Generative Modeling
ICLR 2024	PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying physics, making accurate predictions difficult. We focus on the Numerical Weather Prediction (NWP) post-processing based precipitation forecasting task to couple Machine Learning techniques with traditional NWP. This task remains challenging due to the imbalanced precipitation data and complex relationships between multiple meteorological variables. To address these limitations, we introduce the PostRainBench, a comprehensive multi-variable NWP post-processing benchmark, and CAMT, a simple yet effective Channel Attention Enhanced Multi-task Learning framework with a specially designed weighted loss function. Extensive experimental results on the proposed benchmark show that our method outperforms state-of-the-art methods by 6.3%, 4.7%, and 26.8% in rain CSI and improvements of 15.6%, 17.4%, and 31.8% over NWP predictions in heavy rain CSI on respective datasets. Most notably, our model is the first deep learning-based method to outperform NWP approaches in heavy rain conditions. These results highlight the potential impact of our model in reducing the severe consequences of extreme rainfall events. Our datasets and code are available at https://github.com/yyyujintang/PostRainBench. Authors: Yujin Tang (The Hong Kong University of Science and Technology (Guangzhou)); Jiaming Zhou (Hong Kong University of Science and Technology (Guangzhou)); Xiang Pan (Nanjing University); Zeying Gong (Hong Kong University of Science and Technology (Guangzhou)); Junwei Liang (The Hong Kong University of Science and Technology (Guangzhou))	Extreme Weather Computer Vision & Remote Sensing
ICLR 2024	Physics-informed Machine Learning-based Cloud Microphysics parameterization for Earth System Models (Papers Track) Abstract and authors: (click to expand) Abstract: In this study, we develop a physics-informed machine learning (ML)-based cloud microphysics parameterization for the ICON model. By training the ML parameterization on high-resolution simulation data, we aim to improve Earth System Models (ESMs) in comparison to traditional parameterization schemes. We investigate the usage of a multilayer perceptron (MLP) with feature engineering and physics-constraints, and use explainability techniques to understand the relationship between input features and model output. Our novel approach yields promising results, with the physics-informed ML-based cloud microphysics parameterization achieving an R$^2$ score up to 0.777 for an individual feature. Additionally, we demonstrate a notable improvement in the overall performance in comparison to a baseline MLP, increasing its average R$^2$ score from 0.290 to 0.613 across all variables. This approach to improve the representation of cloud microphysics in ESMs promises to enhance climate projections, contributing to a better understanding of climate change. Authors: Ellen Sarauer (German Aerospace Center (DLR)); Mierk Schwabe (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Philipp Weiss (University of Oxford); Axel Lauer (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Philip Stier (University of Oxford); Veronika Eyring (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany)	Climate Science & Modeling Interpretable ML
ICLR 2024	From spectra to biophysical insights: end-to-end learning with a biased radiative transfer model (Papers Track) Abstract and authors: (click to expand) Abstract: Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a challenge, particularly in understanding forests' biophysical reactions to climate change. Traditional methods in remote sensing that invert radiative transfer models (RTMs) to retrieve biophysical variables from spectral data often fail to account for biases inherent in the RTM, especially for complex forests. We propose to integrate RTMs into an auto-encoder architecture, creating an end-to-end learning approach. Our method not only corrects biases in RTMs but also outperforms traditional techniques for variable retrieval like neural network regression. Furthermore, our framework has potential generally for inverting biased physical models. Authors: Yihang She (University of Cambridge); Clement Atzberger (Mantle Labs); Andrew Blake (University of Cambridge, Mantle Labs); Srinivasan Keshav (University of Cambridge)	Earth Observation & Monitoring Interpretable ML
ICLR 2024	Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Building Energy Rating (BER) stands as a pivotal metric, enabling building owners, policymakers, and urban planners to understand the energy-saving potential through improving building energy efficiency. As such, enhancing buildings' BER levels is expected to directly contribute to the reduction of carbon emissions and promote climate improvement. Nonetheless, the BER assessment process is vulnerable to missing and inaccurate measurements. In this study, we introduce CLEAR, a data-driven approach designed to scrutinize the inconsistencies in BER assessments through self-supervised contrastive learning. We validated the effectiveness of CLEAR using a dataset representing Irish building stocks. Our experiments uncovered evidence of inconsistent BER assessments, highlighting measurement data corruption within this real-world dataset. Authors: Qian Xiao (Trinity College Dublin); Dan Liu (Trinity College Dublin); Kevin Credit (Maynooth University)	Buildings Data Mining Interpretable ML Unsupervised & Semi-Supervised Learning
ICLR 2024	Interpretable Machine Learning for Extreme Events detection: An application to droughts in the Po River Basin (Papers Track) Abstract and authors: (click to expand) Abstract: The increasing frequency and intensity of drought events-periods of significant decrease in water availability-are among the most alarming impacts of climate change. Monitoring and detecting these events is essential to mitigate their impact on our society. However, traditional drought indices often fail to accurately detect such impacts as they mostly focus on single precursors. In this study, we leverage machine learning algorithms to define a novel data-driven, impact-based drought index reproducing as target the Vegetation Health Index, a satellite signal that directly assesses the vegetation status. We first apply novel dimensionality reduction methods that allow for interpretable spatial aggregation of features related to precipitation, temperature, snow, and lakes. Then, we select the most informative and non-redundant features through filter feature selection. Finally, linear supervised learning methods are considered, given the small number of samples and the aim of preserving interpretability. The experimental setting focuses on ten sub-basins of the Po River basin, but the aim is to design a machine learning-based workflow applicable on a large scale. Authors: Paolo Bonetti (Politecnico di Milano); Matteo Giuliani (Politecnico di Milano); Veronica Cardigliano (Politecnico di Milano); Alberto Maria Metelli (Politecnico di Milano); Marcello Restelli (Politecnico di Milano); Andrea Castelletti (Politecnico di Milano)	Agriculture & Food Extreme Weather Interpretable ML
ICLR 2024	Literature Mining with Large Language Models to Assist the Development of Sustainable Building Materials (Papers Track) Abstract and authors: (click to expand) Abstract: Concrete industry, as one of the significant sources of carbon emissions, drives the urgency for its decarbonization that requires a shift to alternative materials. However, the absence of systematic knowledge summary remains a challenge for further development of sustainable building materials. This work offers a cost-efficient strategy for information extraction tasks in complex terminology settings using small (2.8B) large language models (LLMs) with well-designed instruction-completion schemes and fine-tuning strategies, introducing a dataset cataloging civil engineering applications of alternative materials. The Multiple Choice instruction scheme significantly improves model accuracies in entity inference from non-Noun-Phrase sources, with supervised fine-tuning benefiting from straightforward tokenized representations of choices. We also demonstrate the utility of the dataset by extracting valuable insights into promising applications of alternative materials from knowledge graph representations. Authors: Yifei Duan (Massachusetts Institute of Technology); Yixi Tian (Massachusetts Institute of Technology); Soumya Ghosh (IBM Research); Richard Goodwin (IBM T.J. Watson Research Center); Vineeth Venugopal (Massachusetts Institute of Technology); Jeremy Gregory (Massachusetts Institute of Technology); Jie Chen (IBM Research); Elsa Olivetti (Massachusetts Institute of Technology)	Natural Language Processing Buildings Chemistry & Materials
ICLR 2024	A Deep Learning Technology Suite for Cost-Effective Sequestered CO2 Monitoring (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon capture and storage (CCS) is a way of reducing carbon emissions to help tackle global warming. Injecting CO2 into rock formations and preventing it from escaping to the surface is a main step in a CCS project. Therefore, monitoring of geologically sequestered CO2 is important for CCS security assessment. Time-lapse seismic (4D seismic) is one of the most effective tools for CO2 monitoring. Unfortunately, the main challenge of 4D seismic is the high cost due to repeated monitoring seismic data acquisition surveys and the subsequent time-consuming data processing that involves imaging and inversion. To address this, we developed a technology suite powered by deep learning engines that significantly reduces the cost by (1) acquiring very sparse monitoring data; (2) firing multiple seismic sources simultaneously; (3) converting 2D images to 3D volume; (4) enforcing repeatability between baseline data and monitoring data; and (5) nonlinearly mapping seismic data to subsurface property model to bypass complex wave-equation-based seismic data processing procedures. Authors: Wenyi Hu (SLB); Son Phan (SLB); Cen Li (SLB); Aria Abubakar (SLB)	Carbon Capture & Sequestration Earth Observation & Monitoring Climate Science & Modeling Hybrid Physical Models Unsupervised & Semi-Supervised Learning
ICLR 2024	On the potential of Optimal Transport in Geospatial Data Science (Papers Track) Abstract and authors: (click to expand) Abstract: Prediction problems in geographic information science and transportation are often motivated by the possibility to enhance operational efficiency and thereby reduce emissions. Examples range from predicting car sharing demand for relocation planning to forecasting traffic congestion for navigation purposes. However, conventional accuracy metrics ignore the spatial distribution of the errors, despite its relevance for operations. Here, we put forward a spatially aware evaluation metric and loss function based on Optimal Transport (OT). Our framework leverages partial OT and can minimize relocation costs in any spatial prediction problem. We showcase the advantages of OT-based evaluation over conventional metrics and further demonstrate the application of an OT loss function for improving forecasts of bike sharing demand and charging station occupancy. Thus, our framework not only aligns with operational considerations, but also signifies a step forward in refining predictions within geospatial applications. All code is available at https://github.com/mie-lab/geospatial_optimal_transport. Authors: Nina V Wiedemann (ETH Zurich); Martin Raubal (ETH Zürich)	Time-series Analysis Climate Science & Modeling Transportation
ICLR 2024	ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability (Papers Track) Abstract and authors: (click to expand) Abstract: The Environmental Extended Multi-Regional Input-Output analysis is the predominant Ecological Economic research framework for analysing the environmental impact of economic activities. This paper introduces the novel ExioML dataset as the first Machine Learning benchmark data in sustainability analysis. We open-sourced the ExioML data and development toolkit to lower barriers and accelerate the cooperation between Machine Learning and Ecological Economic research. A crucial greenhouse gas emission regression task evaluates the usability of the proposed dataset. We compared the performance of traditional shallow models against deep models by leveraging a diverse factor accounting table and incorporating multiple modalities of categorical and numerical features. Our findings reveal that deep and ensemble models achieve low mean square errors below 0.25 and serve as a future machine learning research baseline. Through Ex- ioML, we aim to foster precise ML predictions and modelling to support climate actions and sustainable investment decisions. The data and codes are available: https://github.com/Yvnminc/ExioML Authors: Yanming Guo (University of Sydney)	Climate Finance & Economics
ICLR 2024	FARADAY: SYNTHETIC SMART METER GENERATOR FOR THE SMART GRID (Papers Track) Abstract and authors: (click to expand) Abstract: Access to smart meter data is essential to rapid and successful transitions to elec- trified grids, underpinned by flexibility delivered by low carbon technologies, such as electric vehicles (EV) and heat pumps, and powered by renewable energy. Yet little of this data is available for research and modelling purposes due consumer privacy protections. Whilst many are calling for raw datasets to be unlocked through regulatory changes, we believe this approach will take too long. Synthetic data addresses these challenges directly by overcoming privacy issues. In this pa- per, we present Faraday, a Variational Auto-encoder (VAE)-based model trained over 300 million smart meter data readings from an energy supplier in the UK, with information such as property type and low carbon technologies (LCTs) own- ership. The model produces household-level synthetic load profiles conditioned on these labels, and we compare its outputs against actual substation readings to show how the model can be used for real-world applications by grid modellers interested in modelling energy grids of the future. Authors: Sheng Chai (Centre for Net Zero); Gus Chadney (Centre for Net Zero)	Generative Modeling
ICLR 2024	Generalizable Temperature Nowcasting with Physics-Constrained RNNs for Predictive Maintenance of Wind Turbine Components (Papers Track) Abstract and authors: (click to expand) Abstract: Machine learning plays an important role in the operation of current wind energy production systems. One central application is predictive maintenance to increase efficiency and lower electricity costs by reducing downtimes. Integrating physics-based knowledge in neural networks to enforce their physical plausibilty is a promising method to improve current approaches, but incomplete system information often impedes their application in real world scenarios. We describe a simple and efficient way for physics-constrained deep learning-based predictive maintenance for wind turbine gearbox bearings with partial system knowledge. The approach is based on temperature nowcasting constrained by physics, where unknown system coefficients are treated as learnable neural network parameters. Results show improved generalization performance to unseen environments compared to a baseline neural network, which is especially important in low data scenarios often encountered in real-world applications. Authors: Johannes Exenberger (Graz University of Technology); Matteo Di Salvo (Sirius Energy Automation); Thomas Hirsch (Graz University of Technology); Franz Wotawa (Graz University of Technology, Institute for Software Technology); Gerald Schweiger (Graz University of Technology)	Hybrid Physical Models Time-series Analysis
ICLR 2024	Forecasting Tropical Cyclones with Cascaded Diffusion Models (Papers Track) Abstract and authors: (click to expand) Abstract: As tropical cyclones become more intense due to climate change, the rise of Al-based modelling provides a more affordable and accessible approach compared to traditional methods based on mathematical models. This work leverages generative diffusion models to forecast cyclone trajectories and precipitation patterns by integrating satellite imaging, remote sensing, and atmospheric data. It employs a cascaded approach that incorporates three main tasks: forecasting, super-resolution, and precipitation modelling. The training dataset includes 51 cyclones from six major tropical cyclone basins from January 2019 - March 2023. Experiments demonstrate that the final forecasts from the cascaded models show accurate predictions up to a 36-hour rollout, with excellent Structural Similarity (SSIM) and Peak-Signal-To-Noise Ratio (PSNR) values exceeding 0.5 and 20 dB, respectively, for all three tasks. The 36-hour forecasts can be produced in as little as 30 mins on a single Nvidia A30/RTX 2080 Ti. This work also highlights the promising efficiency of Al methods such as diffusion models for high-performance needs in weather forecasting, such as tropical cyclone forecasting, while remaining computationally affordable, making them ideal for highly vulnerable regions with critical forecasting needs and financial limitations. Code accessible at \url{https://github.com/nathzi1505/forecast-diffmodels}. Authors: Pritthijit Nath (Imperial College London); Pancham Shukla (Imperial College London); Shuai Wang (University of Delaware); Cesar Quilodran-Casas (Imperial College London)	Generative Modeling Extreme Weather
ICLR 2024	Forecasting regional PV power in Great Britain with a multi-modal late fusion network (Papers Track) Abstract and authors: (click to expand) Abstract: The ability to forecast solar photovoltaic (PV) power is important for grid balancing and reducing the CO2 intensity of electricity globally. The use of multi-modal data such as numerical weather predictions (NWPs) and satellite imagery can be harnessed to make more accurate PV forecasts. In this work, we propose a late fusion model which integrates two different NWP sources alongside satellite images to make 0-8 hour lead time forecasts for grid regions across Great Britain. We limit the model inputs to be reflective of those available in a live production system. We show how the different input data sources contribute to average error at each time horizon and compare against a simple baseline. Authors: James Fulton (Open Climate Fix); Jacob Bieker (Open Climate Fix); Peter Dudfield (Open Climate Fix); Solomon Cotton (Open Climate Fix); Zakari Watts (Open Climate Fix); Jack Kelly (Open Climate Fix)	Power & Energy Computer Vision & Remote Sensing
ICLR 2024	Exploring Graph Neural Networks to Predict the Seagrasses Ecosystem State in the Italian Seas (Papers Track) Abstract and authors: (click to expand) Abstract: Marine coastal ecosystems (MCEs) play a critical role in climate change adaptation and human well-being. However, they face global threats from environmental pressures, both related to climate change (CC) and direct human impacts. Leveraging the increasing availability of geospatial data, this study explores Graph Neural Networks (GNNs) to assess cumulative impacts arising from human and CC related pressures on the Seagrass ecosystem in the Italian seas. Unlike traditional machine learning (ML) models with which they were compared in this study, GNNs incorporate the spatial component of data through graph structures. While experimental results demonstrate a modest performance improvement in GNNs, the study is constrained by limited data availability, preventing the exploration of the temporal component and physical laws representable through graph structures. Future efforts aim to collect higher-resolution spatial and temporal data, considering expressible environmental processes, to enhance model learning. Authors: Angelica Bianconi (University School for Advanced Studies (IUSS) Pavia & Ca’ Foscari University of Venice); Sebastiano Vascon (Ca' Foscari University of Venice & European Centre for Living Technology); Elisa Furlan (Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) & Ca' Foscari University of Venice); Andrea Critto (Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) & Ca' Foscari University of Venice)	Ecosystems & Biodiversity Oceans & Marine Systems
ICLR 2024	Global High Resolution CO2 monitoring using Super Resolution (Papers Track) Abstract and authors: (click to expand) Abstract: Monitoring Greenhouse Gases (GHGs) concentrations and emissions is essential to mitigate climate change. Thanks to the large amount of satellite data available, it is now possible to understand GHGs' behaviours at a broad scale. However, due to remote sensing devices technological limitations, the task of global high resolution (HR) monitoring remains an open problem. To avoid waiting for new missions and better data to be generated, it is therefore relevant to experiment with processing methods able to improve existing datasets. Our paper proposes to apply Super Resolution (SR), a Deep Learning (DL) approach commonly used in Computer Vision (CV), on global L3 satellite data. We produce a daily high resolution global CO2 dataset that opens the door for globally consistent point source monitoring. Authors: Andrianirina Rakotoharisoa (Imperial College London); Rossella Arcucci (Imperial College London)	Computer Vision & Remote Sensing Earth Observation & Monitoring
ICLR 2024	DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations (Papers Track) Abstract and authors: (click to expand) Abstract: This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which reveal a qualitatively realistic superposition of convectively coupled wave modes in the tropics. Cross-spectral analysis confirms successful generation of low frequency variations associated with the Madden--Julian oscillation, which regulates most subseasonal to seasonal predictability in the observed atmosphere, and convectively coupled moist Kelvin waves with approximately correct dispersion relationships. Despite secondary issues and biases, the results affirm the potential for a next generation of global diffusion models trained on increasingly sparse, and increasingly direct and differentiated observations of the world, for practical applications in subseasonal and climate prediction. Authors: Jason Stock (Colorado State University); Jaideep Pathak (NVIDIA Corporation); Yair Cohen (NVIDIA Corporation); Mike Pritchard (NVIDIA Corporation); Piyush Garg (NVIDIA); Dale Durran (NVIDIA Corporation); Morteza Mardani (NVIDIA Corporation); Noah D Brenowitz (NVIDIA)	Climate Science & Modeling Computer Vision & Remote Sensing Generative Modeling
ICLR 2024	A cautionary tale about deep learning-based climate emulators (Papers Track) Abstract and authors: (click to expand) Abstract: Climate models are computationally too expensive for many tasks, such as, rapidly exploring future impacts of climate policies. Thus, since the 1980s scientists have been developing lightweight approximations or emulators of climate models. Recently, deep learning has been proposed for this task and most commonly been evaluated on the benchmark ClimateBenchv1.0. We implemented a linear regression-based model from the 1990s with 30K parameters, called linear pattern scaling, that is now the 'best' model on ClimateBenchv1.0 -- outperforming the incumbent 100M-parameter foundation model, ClimaX, on the spatial error of 3 out of the 4 variables. Nevertheless, climate emulation might benefit from innovations in machine learning and we analyse two aspects that need to be addressed in future emulators: First, the data complexity depends strongly on the climate variable of interest and the chosen spatiotemporal resolution. Second, current benchmarks do not sufficiently address the large impact of interannual variability in the climate system. We have published our analysis as an interactive tutorial at github.com/ygaxolotl/tags-linear-pattern-scaling. Authors: Björn Lütjens (Massachussets Institute of Technology); Raffaele Ferrari (Massachusetts Institute of Technology); Paolo Giani (Massachusetts Institute of Technology); Dava Newman (MIT); Andre Souza (Massachusetts Institute of Technology); Duncan Watson-Parris (University of California San Diego); Noelle Selin (Massachusetts Institute of Technology)	Climate Science & Modeling Hybrid Physical Models Time-series Analysis
ICLR 2024	CausalPrompt: Enhancing LLMs with Weakly Supervised Causal Reasoning for Robust Performance in Non-Language Tasks (Papers Track) Abstract and authors: (click to expand) Abstract: In confronting the pressing issue of climate change, we introduce "CausalPrompt", an innovative prompting strategy that adapts large language models (LLMs) for classification and regression tasks through the application of weakly supervised causal reasoning. We delve into the complexities of data shifts within energy systems, often resulting from the dynamic evolution of sensor networks, leading to discrepancies between training and test data distributions or feature inconsistencies. By embedding domain-specific reasoning in the finetuning process, CausalPrompt significantly bolsters the adaptability and resilience of energy systems to these shifts. We show that CausalPrompt significantly enhances predictions in scenarios characterized by feature shifts, including electricity demand, solar power generation, and cybersecurity within energy infrastructures. This approach underlines the crucial role of CausalPrompt in enhancing the reliability and precision of predictions in energy systems amid feature shifts, highlighting its significance and potential for real-world applications in energy management and cybersecurity, contributing effectively to climate change mitigation efforts. Authors: Tung-Wei Lin (University of California, Berkeley); Vanshaj Khattar (Virginia Tech); Yuxuan Huang (University College London); Junho Hong (University of Michigan); Ruoxi Jia (Virginia Tech); Chen-Ching Liu (Virginia Tech); Alberto L Sangiovanni-Vincentelli (University of California, Berkeley); Ming Jin (Virginia Tech)	Natural Language Processing Buildings Power & Energy
ICLR 2024	Estimating the age of buildings from satellite and morphological features to create a pan-EU Digital Building Stock Model (Papers Track) Abstract and authors: (click to expand) Abstract: The acceleration in the effects of global warming and the recent turbulences in the energy market are further highlighting the need to act quicker and smarter in terms of decisions to transition to greener energy and reduce our overall energy consumption. With buildings accounting for about 40% of the energy consumption in Europe, it is crucial to have a comprehensive understanding of the building stock and their energy-related characteristics, including their age, in order to make informed decisions for energy savings. This study introduces a novel way to approach the age estimation of buildings at scale, using a machine learning method that integrates satellite-based imagery with morphological features of buildings. The findings demonstrate the benefits of combining these data sources and underscore the importance of incorporating local data to enable accurate prediction across different cities. Authors: Jeremias Wenzel (Universiteit Twente); Ana M. Martinez (European Commission - Joint Research Centre); Pietro Florio (European Commission - Joint Research Centre); Katarzyna Goch (Institute of Geography and Spatial Organization Polish Academy of Sciences)	Buildings Cities & Urban Planning
ICLR 2024	Probabilistic electricity price forecasting through conformalized deep ensembles (Papers Track) Abstract and authors: (click to expand) Abstract: Probabilistic electricity price forecasting (PEPF) is subject of an increasing interest, following the demand for proper prediction uncertainty quantification, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles (DE) have been recently shown to outperform state of the art PEPF benchmarks. Still, they require reliability improvements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we tackle this issue by extending the DE framework with the introduction of a Conformal Prediction based technique. Experiments have been conducted on multiple market regions, achieving day-ahead probabilistic forecasts with better hourly coverage. Authors: Alessandro Brusaferri (National Research Council of Italy); Andrea Ballarino (National Research Council of Italy); Luigi Grossi (University of Parma); Fabrizio Laurini (University of Parma)	Power & Energy
ICLR 2024	Interpretable Machine Learning for power systems: Establishing Confidence in SHapley Additive exPlanationS (Papers Track) Abstract and authors: (click to expand) Abstract: Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This work first seeks to showcase the benefits of SHapley Additive exPlanations (SHAP) for understanding the outcomes of ML models, which are increasingly being used to optimise power systems with increasing share of Renewable Energy (RE), to support worldwide calls for decarbonisation and climate change. To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF)—a power system physics-based linear sensitivity index—can be derived from the SHAP values. To do so, we take the derivatives of SHAP values from a ML model trained to learn line-flows from generator power-injections, using a DC power-flow case in a benchmark test network. In demonstrating that SHAP values can be related back to the physics that underpin the power system, we build confidence in the explanations SHAP can offer. Authors: Tabia Ahmad (University of Strathclyde); Robert Hamilton (Shell); Panagiotis Papadopoulos (University of Manchester); Samuel Chevalier (University of Vermont); Ilgiz Murzakhanov (Technical University of Denmark); Rahul Nellikkath (Technical University of Denmark); Jochen Bernhard Stiasny (Technical University of Denmark); Spyros Chatzivasileiadis (Technical University of Denmark)	Power & Energy Interpretable ML
ICLR 2024	DETECTION OF METEOROLOGICAL VARIABLES IN A WIND FARM INFLUENCING THE EXTREME WIND SPEED BY HETEROGENEOUS GRANGER CAUSALITY (Papers Track) Abstract and authors: (click to expand) Abstract: For an efficiently managed wind farm and wind power generation under adverse weather, knowledge of meteorological parameters influencing wind speed is of crucial importance for optimized and improved forecasts. We investigate temporal effects of wind speed related processes such as wakes within the wind farm using the Heterogeneous Graphical Granger model. The ERA5 meteorological reanalysis was used to generate wind farm power production data in Eastern Austria. We evaluated six different scenarios for the hydrological half-year period, based on moderate wind speed and varying temporal intervals of low or high extreme wind speed This allows to carry out causal reasoning about possible causes of extreme wind speed in a wind farm. A set of causal parameters for each of the scenarios was discovered enabling future early warning and for taking management measures for wind farm power generation management under adverse weather conditions. Authors: Katerina Schindlerova (UniVie); Irene Schicker (Geos); Kejsi Hoxhallari (UniVie); Claudia Plant (University of Vienna, Austria)	Causal & Bayesian Methods Extreme Weather
ICLR 2024	Machine Learning for the Detection of Arctic Melt Ponds from Infrared Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Melt ponds are pools of water on Arctic summer sea ice that play an important role in the Arctic climate system. Retrieving their coverage is essential to better understand and predict the rapidly changing Arctic, but current data are limited. The goal of this study is to enhance melt pond data by developing a method that segments thermal infrared (TIR) airborne imagery into melt pond, sea ice, and ocean classes. Due to temporally and spatially varying surface temperatures, we use a data-driven deep learning approach to solve this task. We adapt and fine-tune AutoSAM, a Segment Anything-based segmentation model. We make the code, data, and models available online. Authors: Marlena Reil (University of Osnabrück and University of Bremen, Institute of Environmental Physics); Gunnar Spreen (University of Bremen, Institute of Environmental Physics); Marcus Huntemann (University of Bremen, Institute of Environmental Physics); Lena Buth (Alfred Wegener Institute); Dennis Wilson (University of Toulouse, ISAE-Supaero)	Earth Observation & Monitoring Computer Vision & Remote Sensing
ICLR 2024	Identifying Complex Dynamics of Power Grid Frequency (Papers Track) Abstract and authors: (click to expand) Abstract: The energy system is undergoing rapid changes to integrate a growing number of intermittent renewable generators and facilitate the broader transition toward sustainability. As millions of consumers and thousands of (volatile) generators are connected to the same synchronous grid, no straightforward bottom-up models describing the dynamics are available on a continental scale comprising all of these necessary details. Hence, to identify this unknown power grid dynamics, we propose to leverage the Sparse Identification of Nonlinear Dynamics (SINDy) method. Thereby, we unveil the governing equations underlying the dynamical system directly from data measurements. Investigating the power grids of Iceland, Ireland and the Balearic islands as sample systems, we observe structurally similar dynamics with remarkable differences in both quantitative and qualitative behavior. Overall, we demonstrate how complex, i.e. non-linear, noisy, and time-dependent, dynamics can be identified straightforwardly. Authors: Xinyi Wen (Karlsruhe Institute of Technology); Ulrich Oberhofer (Karlsruhe Institute of Technology); Leonardo Rydin Gorjão (Norwegian University of Life Sciences); G.Cigdem YALCIN (Istanbul University); Veit Hagenmeyer (Karlsruhe Institute of Technology (KIT)); Benjamin Schäfer (Karlsruhe Institute of Technology)	Power & Energy Time-series Analysis
ICLR 2024	Imbalance-aware Presence-only Loss Function for Species Distribution Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on various large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations. Authors: Robin Zbinden (EPFL); Nina van Tiel (EPFL); Marc Rußwurm (Wageningen University); Devis Tuia (EPFL)	Ecosystems & Biodiversity
ICLR 2024	Deep Gaussian Processes and inversion for decision support in model-based climate change mitigation and adaptation problems (Papers Track) Abstract and authors: (click to expand) Abstract: To inform their decisions, policy makers often rely on models developed by researchers that are computationally intensive and complex and that frequently run on High Performance Computers (HPC). These decision-support models are not used directly by deciders and the results of these models tend to be presented by experts as a limited number of potential scenarios that would result from a limited number of potential policy choices. Machine learning models such as Deep Gaussian Processes (DGPs) can be used to radically re-define how decision makers can use models by creating a ‘surrogate model’ or ‘emulator’ of the original model. Surrogate models can then be embedded into apps that decisions makers can use to directly explore a vast array of policy options corresponding to potential target outcomes (model inversion). To illustrate the mechanism, we give an example of application that is envisaged as part of the UK government’s Net Zero strategy. To achieve Net Zero CO2 emissions by 2050, the UK government is considering multiple options that include planting trees to capture carbon. However, the amount of CO2 captured by the trees depend on a large number of factors that include climate conditions, soil type, soil carbon, tree type, ... Depending on these factors the net balance of carbon removal after planting trees may not necessarily be positive. Hence, choosing the right place to plant the right tree is very important. A decision-helping model has been developed to tackle this problem. For a given policy input, the model outputs its impact in terms of CO2 sequestration, biodiversity and other ecosystem services. We show how DGPs can be used to create a surrogate model of this original afforestation model and how these can be embedded into an R shiny app that can then be directly used by decision makers. Authors: bertrand nortier (University of Exeter); daniel williamson (University of Exeter); mattia mancini (University of Exeter); amy binner (University of Exeter); brett day (University of Exeter); ian bateman (University of Exeter)	Public Policy Carbon Capture & Sequestration Ecosystems & Biodiversity Forests Causal & Bayesian Methods Uncertainty Quantification & Robustness
ICLR 2024	Generalized Policy Learning for Smart Grids: FL TRPO Approach (Papers Track) Abstract and authors: (click to expand) Abstract: The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs. Our approach reveals latent interconnections and employs personalized encoding methods to capture unique insights, understanding the relationships between features and optimal strategies, allowing our model to generalize to previously unseen data. Experimental results validate the robustness of our approach, affirming its proficiency in effectively learning policy models for smart grid challenges. Authors: Yunxiang LI (MBZUAI); Nicolas M Cuadrado (MBZUAI); Samuel Horváth (MBZUAI); Martin Takac (Mohamed bin Zayed University of Artificial Intelligence)	Power & Energy Reinforcement Learning
ICLR 2024	A Deep Learning Framework to Efficiently Estimate Precipitation at the Convection Permitting Scale (Papers Track) Abstract and authors: (click to expand) Abstract: Precipitation-related extreme events are rapidly growing due to climate change, emphasizing the need for accurate hazard projections. To effectively model the convective phenomena driving severe precipitation, high-resolution estimates are crucial. Existing methods struggle with either insufficient expressiveness in capturing complex convective dynamics, due to the low resolution, or excessive computational demands. In response, we propose an innovative deep learning framework that efficiently harnesses available data to yield precise results. This model, based on graph neural networks, utilises two grids with different resolution and two sets of edges to represent spatial relationships. Employing as input ERA5 reanalysis atmospheric variables on an approximately 25 km grid, the framework produces hourly precipitation estimates on a finer 3 km grid. Findings are promising in accurately capturing yearly precipitation distribution and estimating cumulative precipitation during extreme events. Notably, the model demonstrates effectiveness in spatial regions not included in the training, motivating further exploration of its transferability potential. Authors: Valentina Blasone (University of Trieste); Erika Coppola (Earth System Physics Section, ICTP, Trieste); Guido Sanguinetti (SISSA); Viplove Arora (Theoretical and Scientific Data Science, SISSA, Trieste); Serafina Di Gioia (Earth System Physics Section, ICTP, Trieste); Luca Bortolussi (University of Trieste)	Climate Science & Modeling Extreme Weather
ICLR 2024	Global Vegetation Modeling With Pre-Trained Weather Transformers (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate vegetation models can produce further insights into the complex inter-action between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecasting, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation of the atmosphere’s state can be transferred to model the normalized difference vegetation index (NDVI). Our model globally estimates vegetation activity at a resolution of 0.25◦ while relying only on meteorological data. We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch. Additionally, we compare our results to other recent data-driven NDVI modeling approaches from machine learning and ecology literature. We further provide experimental evidence on how much data and training time is necessary to turn FourCastNet into an effective vegetation model. Code and models are available at https://github.com/LSX-UniWue/Global-Ecosystem-Modeling. Authors: Pascal Janetzky (University Wuerzburg); Florian Gallusser (Universität Würzburg); Simon Hentschel (Julius-Maximilians-Universität of Würzburg); Andreas Hotho (University of Wuerzburg); Anna Krause (Universität Würzburg, Department of Computer Science, CHair X Data Science)	Ecosystems & Biodiversity Climate Science & Modeling Data Mining
ICLR 2024	Building Ocean Climate Emulators (Papers Track) Abstract and authors: (click to expand) Abstract: The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nascent despite the large potential for accelerating coupled climate simulations and improving ocean forecasts on all timescales. There are several fundamental questions to address that can facilitate the creation of ocean emulators. Here we focus on two questions: 1) the role of the atmosphere in improving the extended skill of the emulator and 2) the representation of variables with distinct timescales (e.g., velocity and temperature) in the design of any emulator. In tackling these questions, we show stable prediction of surface fields for over 8 years, training and testing on data from a high-resolution coupled climate model, using results from four regions of the globe. Our work lays out a set of physically motivated guidelines for building ocean climate emulators. Authors: Adam Subel (New York University); Laure Zanna (New York University)	Climate Science & Modeling
ICLR 2024	Neural Tree Reconstruction for the Open Forest Observatory (Papers Track) Abstract and authors: (click to expand) Abstract: The Open Forest Observatory (OFO) is a collaboration across universities and other partners to make low-cost forest mapping accessible to ecologists, land managers, and the general public. The OFO is building both a database of geospatial forest data as well as open-source methods and tools for forest mapping by un- crewed aerial vehicle. Such data are useful for a variety of climate applications including prioritizing reforestation efforts, informing wildfire hazard reduction, and monitoring carbon sequestration. In the current iteration of the OFO’s forest map database, 3D tree maps are created using classical structure-from-motion techniques. This approach is prone to artifacts, lacks detail, and has particular difficulty on the forest floor where the input data (overhead imagery) has limited visibility. These reconstruction errors can potentially propagate to the down- stream scientific tasks (e.g. a wildfire simulation.) Advances in 3D reconstruction, including methods like Neural Radiance Fields (NeRF), produce higher quality results that are more robust to sparse views and support data-driven priors. We explore ways to incorporate NeRFs into the OFO dataset, outline future work to support even more state-of-the-art 3D vision models, and describe the importance of high-quality 3D reconstructions for forestry applications. Authors: Marissa Ramirez de Chanlatte (UC Berkeley); Arjun Rewari (Darrell Group, Berkeley AI Research Lab); Trevor Darrell (UC Berkeley); Derek Young (University of California Davis)	Forests Carbon Capture & Sequestration
ICLR 2024	Graph Neural Network Based Screening of Metal-Organic Frameworks for CO2 Capture (Papers Track) Abstract and authors: (click to expand) Abstract: Our ability to capture and remove carbon dioxide (CO2) at gigaton scale within a decade or two depends on our ability to quickly identify new materials that are high performing, selective over other gases with low energy demand and then further develop them for large scale deployment. As a proven technology for gas separation in other industrial applications, metal-organic frameworks (MOF) come in virtually unlimited number of crystal combinations in their highly porous lattice and may offer the solution for CO2 capture from atmosphere or industrial point sources. Although MOFs can have highly complex crystal structure, which cannot be easily exploited in tabular data format in conventional ML methods or more recent Deep Learning methods, Graph Neural Networks can easily be trained on their representative crystallographic information file (CIF) content. In this work, we train GNNs to create an end-to-end workflow to screen large number of MOF crystal structures directly from the data within the crystallographic information files for their CO2 working capacity or CO2/N2 selectivity under low-pressure conditions. Our preliminary results show that a simple 2-layered Graph Convolution Networks (GCN) can easily achieve R2 score in the range of 0.87 to 0.89, easily. Authors: Zikri Bayraktar (Schlumberger Doll Research); Mengying Li (Schlumberger Doll Research); Shahnawaz Molla (Schlumberger Doll Research)	Carbon Capture & Sequestration
ICLR 2024	Predicting Species Occurrence Patterns from Partial Observations (Papers Track) Abstract and authors: (click to expand) Abstract: To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are changing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur. Authors: Hager Radi Abdelwahed (Mila: Quebec AI Institute); Mélisande Teng (Mila, Université de Montréal); David Rolnick (MIT)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
ICLR 2024	Towards Scalable Deep Species Distribution Modelling using Global Remote Sensing (Papers Track) Abstract and authors: (click to expand) Abstract: Destruction of natural habitats and anthropogenic climate change are threatening biodiversity globally. Addressing this loss necessitates enhanced monitoring techniques to assess the impact of environmental shifts and to guide policy-making efforts. Species distribution models are crucial tools that predict species locations by interpolating observed field data with environmental information. We develop an improved, scalable method for species distribution modelling by proposing a dataset pipeline that incorporates global remote sensing imagery, land use classification data, environmental variables, and observation data, and utilising this with convolutional neural network (CNN) models to predict species presence at higher spatial and temporal resolutions than well-established species distribution modelling methods. We apply our approach to modelling Protea species distributions in the Cape Floristic Region of South Africa, demonstrating its performance in a region of high biodiversity. We train two CNN models and compare their performance to Maxent, a popular conventional species distribution modelling method. We find that the CNN models trained with remote sensing data outperform Maxent, underscoring the potential of our method as an effective and scalable solution for modelling species distribution. Authors: Emily Morris (University of Cambridge); Anil Madhavapeddy (University of Cambridge); Sadiq Jaffer (University of Cambridge); David Coomes (University of Cambridge)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
ICLR 2024	Valuation and Profit Allocation for Electric Vehicle Battery Data in a Data Market (Proposals Track) Abstract and authors: (click to expand) Abstract: This paper delves into the realm of electric vehicle (EV) battery data trading markets, focusing on data valuation and revenue allocation. In the face of fast-developing electric mobility, the safety of EV batteries becomes more and more important, driving the need for robust anomaly detection models. For newly found EV companies lacking extensive data, data markets offer a solution, facilitated by trading platforms. We shape this landscape, outline a transaction process involving data buyers, data sellers, and platforms. Our exploration extends to data valuation methodologies, encompassing the classic Shapley value and the least core algorithm. Considering the complicated mechanisms in EV battery, we unveil a deep learning framework for anomaly detection, treating EV batteries as dynamic systems. To explain data value from an economic perspective, we utilize a utility function considering the direct economic costs saved for the EV company to refine the evaluation process. Based on data value, we further propose revenue allocation schemes to allocate part of EV company's revenue to data sellers, offering diverse perspectives on fair and equitable profit distribution. A case study is conducted based on real world EV battery dataset to illustrate how the different revenue allocation schemes allocate payoffs to data sellers. Authors: Junkang Chen (Peking University); Guannan He (Peking University)	Data Mining Transportation Time-series Analysis
ICLR 2024	SkyImageNet: Towards a large-scale sky image dataset for solar power forecasting (Proposals Track) Abstract and authors: (click to expand) Abstract: The variability of solar photovoltaic (PV) output, particularly that caused by rapidly changing cloud dynamics, challenges the reliability of renewable energy systems. Solar forecasting based on cloud observations collected by ground-level sky cameras shows promising performance in anticipating short-term solar power fluctuations. However, current deep learning methods often rely on a single dataset with limited sample diversity for training, and thus generalize poorly to new locations and different sky conditions. Moreover, the lack of a standardized dataset hinders the consistent comparison of existing solar forecasting methods. To close these gaps, we propose to build a large-scale standardized sky image dataset --- SkyImageNet --- by assembling, harmonizing, and processing suitable open-source datasets collected in various geographical locations. An accompanying python package will be developed to streamline the process of utilizing SkyImageNet in a machine learning framework. We hope that the outcomes of this project will foster the development of more robust forecasting systems, advance the comparability of short-term solar forecasting model performances, and further facilitate the transition to the next generation of sustainable energy systems. Authors: Yuhao Nie (Massachusetts Institute of Technology); Quentin Paletta (European Space Research Institute); Sherrie Wang (MIT)	Computer Vision & Remote Sensing Earth Observation & Monitoring Meta- and Transfer Learning
ICLR 2024	An Adaptive Hydropower Management Approach for Downstream Ecosystem Preservation (Proposals Track) Abstract and authors: (click to expand) Abstract: Hydropower plants play a pivotal role in advancing clean and sustainable energy production, contributing significantly to the global transition towards renewable energy sources. However, hydropower plants are currently perceived both positively as sources of renewable energy and negatively as disruptors of ecosystems. In this work, we highlight the overlooked potential of using hydropower plant as protectors of ecosystems by using adaptive ecological discharges. To advocate for this perspective, we propose using a neural network to predict the minimum ecological discharge value at each desired time. Additionally, we present a novel framework that seamlessly integrates it into hydropower management software, taking advantage of the well-established approach of using traditional constrained optimisation algorithms. This novel approach not only protects the ecosystems from climate change but also contributes to potentially increase the electricity production. Authors: Cecília Coelho (University of Minho); Ming Jin (Virginia Tech); M. Fernanda P. Costa (Dep. Mathematics, University of Minho); Luís L. Ferrás (University of Porto)	Ecosystems & Biodiversity Climate Science & Modeling Power & Energy Time-series Analysis
ICLR 2024	A Benchmark Dataset for Meteorological Downscaling (Proposals Track) Abstract and authors: (click to expand) Abstract: High spatial resolution in atmospheric representations is crucial across Earth science domains, but global reanalysis datasets like ERA5 often lack the detail to capture local phenomena due to their coarse resolution. Recent efforts have leveraged deep neural networks from computer vision to enhance the spatial resolution of meteorological data, showing promise for statistical downscaling. However, methodological diversity and insufficient comparisons with traditional downscaling techniques challenge these advancements. Our study introduces a benchmark dataset for statistical downscaling, utilizing ERA5 and the finer-resolution COSMO-REA6, to facilitate direct comparisons of downscaling methods for 2m temperature, global (solar) irradiance and 100m wind fields. Accompanying U-Net, GAN, and transformer models with a suite of evaluation metrics aim to standardize assessments and promote transparency and confidence in applying deep learning to meteorological downscaling. Authors: Michael Langguth (Juelich Supercomputing Centre - Forschungszentrum Juelich); Paula Harder (Mila); Irene Schicker (Geos); Ankit Patnala (Juelich Supercomputing Centre - Forschungszentrum Juelich); Sebastian Lehner (GeoSphere Austria); Konrad Mayer (GeoSphere Austria); Markus Dabernig (GeoSphere Austria)	Climate Science & Modeling Earth Observation & Monitoring Power & Energy Generative Modeling
ICLR 2024	Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage (Proposals Track) Abstract and authors: (click to expand) Abstract: To address the urgent challenge of climate change, there is a critical need to transition away from fossil fuels towards sustainable energy systems, with renewable energy sources playing a pivotal role. However, the inherent variability of renewable energy, without effective storage solutions, often leads to imbalances between energy supply and demand. Underground Hydrogen Storage (UHS) emerges as a promising long-term storage solution to bridge this gap, yet its widespread implementation is impeded by the high computational costs associated with high fidelity UHS simulations. This paper introduces UHS from a data-driven perspective and outlines a roadmap for integrating machine learning into UHS, thereby facilitating the large-scale deployment of UHS. Authors: Alvaro R Carbonero Gonzales (Los Alamos National Lab); Shaowen Mao (Los Alamos National Laboratory); Mohamed Mehana (Los Alamos National Lab)	Carbon Capture & Sequestration Computer Vision & Remote Sensing
ICLR 2024	Adjustment of ocean carbon sink predictions with an emission-driven Earth system model using a deep neural network (Proposals Track) Abstract and authors: (click to expand) Abstract: Near-term predictions of the Global Carbon Budget (GCB) with Earth system models (ESMs) driven by specified CO2 emissions were used to inform the GCB annual update for the first time in 2023. These predictions are biased because they are initialized indirectly from the ESMs response to physical observational constraints, and because the ESMs themselves are imperfect representations of the climate system. We propose a deep learning-based post-processing method to adjust GCB predictions using an autoencoder, which outperforms standard bias and trend correction methods. Authors: Reinel Sospedra-Alfonso (Environment and Climate Change Canada); Parsa Gooya (Environment and Climate Change Canada); Johannes Exenberger (Graz University of Technology)	Climate Science & Modeling
ICLR 2024	Calibrating Earth System Models with Bayesian Optimal Experimental Design (Proposals Track) Abstract and authors: (click to expand) Abstract: Earth system models (ESMs) are complex climate simulations that are critical for projecting future climate change and its impacts. However, running ESMs is extremely computationally expensive, limiting the number of simulations that can be performed. This results in significant uncertainty in key climate metrics estimated from ESM ensembles. We propose a Bayesian optimal experimental design (BOED) approach to efficiently calibrate ESM simulations to observational data by actively selecting the most informative input parameters. BOED optimises the expected information gain (EIG) to select the ESM input parameter to reduce the final uncertainty estimates in the climate metrics of interest. Initial results on a synthetic benchmark demonstrate our approach can more efficiently reduce uncertainty compared to common sampling schemes like Latin hypercube sampling. Authors: Tim Reichelt (University of Oxford); Shahine Bouabid (University of Oxford); Luke Ong (University of Oxford); Duncan Watson-Parris (University of California San Diego); Tom Rainforth (University of Oxford)	Active Learning Climate Science & Modeling Causal & Bayesian Methods
ICLR 2024	Severe Wind Event Prediction with Multivariate Physics-Informed Deep Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Wind turbines play a crucial role in combating climate change by harnessing the force of the wind to generate clean and renewable energy. One key factor in ensuring the long-term effectiveness of wind turbines is the reduction of operating costs due to maintenance. Severe weather events, such as extreme changes in wind, can damage turbines, resulting in costly maintenance and economic losses in power production. We propose a preliminary physics-informed deep learning model to improve predictions of severe wind events and a multivariate time series extension for this work. Authors: Willa Potosnak (Carnegie Mellon University); Cristian I Challu (Carnegie Mellon University); Kin G. Olivares (Carnegie Mellon University); James K Miller (Carnegie Mellon University); Artur Dubrawski (Carnegie Mellon University)	Hybrid Physical Models Extreme Weather Power & Energy Time-series Analysis
ICLR 2024	Empowering Safe Reinforcement Learning in Power System Control with CommonPower (Tutorials Track) Abstract and authors: (click to expand) Abstract: Reinforcement learning (RL) has become a valuable tool for addressing complex decision-making problems in power system control. However, the unique intricacies of this domain necessitate the development of specialized RL algorithms. While benchmarking problems have proven effective in advancing algorithm development in various domains, existing suites do not enable a systematic study of two key challenges in power system control: ensuring adherence to physical constraints and evaluating the impact of forecast accuracy on controller performance. This tutorial introduces the sophisticated capabilities of the CommonPower toolbox, designed to address these overlooked challenges. We guide participants in composing benchmark problems within CommonPower, leveraging predefined components, and demonstrate the creation of new components. We showcase the training of a safe RL agent to solve a benchmark problem, comparing its performance against a built-in MPC baseline. Notably, CommonPower's symbolic modeling approach enables the automatic derivation of safety shields for vanilla RL algorithms. We explain the theory behind this feature in a concise introduction to the field of safe RL. Furthermore, we present CommonPower's interface for seamlessly integrating diverse forecasting strategies into the system. The workshop emphasizes the significance of safeguarding vanilla RL algorithms and encourages researchers to systematically investigate the influence of forecast uncertainties in their experiments. Authors: Hannah Markgraf (Technical University of Munich); Michael Eichelbeck (Technical University of Munich); Matthias Althoff (Technical University of Munich)	Reinforcement Learning Buildings
ICLR 2024	Planning for Floods & Droughts: Intro to AI-Driven Hydrological Modeling (Tutorials Track) Abstract and authors: (click to expand) Abstract: This tutorial presents an AI-driven hydrological modeling approach to advance predictions of extreme hydrological events, including floods and droughts, which are of significant socioeconomic concerns. Traditionally, physics-based hydrological models have been the mainstay for simulating rainfall-runoff dynamics and forecasting streamflow. These models, while effective, are constrained by limitations in our systematic understanding and an inability to incorporate heterogeneous data. Recently, the surge in availability of multi-scale, multi-modal hydrological data has spurred the adoption of data-driven machine learning (ML) techniques. These methods have shown promising predictive performance. However, they often struggle with generalization and reliability, especially under climate change. This tutorial introduces physics-informed ML, by leveraging data and domain knowledge, to improve prediction accuracy and trustworthiness. We will delve into uncertainty quantification methods for probabilistic predictions that are vital for climate-resilient planning in managing floods and droughts. Participants will be guided through a comprehensive workflow, encompassing data analysis, model construction, and model evaluation. This tutorial is designed to elevate researchers’ understanding of hydrological systems and provide practitioners with robust, climate-resilient water management tools. These tools are instrumental in facilitating informed decision-making, crucial in the context of climate adaptation strategies. Participants will learn: ● Heterogeneous climate and hydrology data analysis ● State-of-the-art neural network models for rainfall-runoff modeling. ● ML model construction, training, validating, and testing ● Multiple ways to build a physics-informed ML model ● Uncertainty quantification in ML model predictions. All code and data will be publicly available for researchers/practitioners to build their own models. Authors: Kshitij Tayal (Oak Ridge National Lab); Arvind Renganathan (University of Minnesota); Dan Lu (Oak Ridge National Laboratory)	Climate Science & Modeling Oceans & Marine Systems Data Mining Time-series Analysis
ICLR 2024	Understanding drivers of climate extremes using regime-specific causal graphs (Tutorials Track) Abstract and authors: (click to expand) Abstract: The climate system is intricate, involving numerous interactions among various components at multiple spatio-temporal scales. This complexity poses a significant challenge in understanding and predicting weather extremes within the Earth's climate system. However, a better understanding of the dynamics of such events is crucial due to their profound impact on ecosystems, economies, and worldwide communities. This tutorial will offer a comprehensive guide on using Regime-PCMCI (Saggioro et al., 2020), a constraint-based causal discovery technique, to uncover the causal relationships governing anomalous climate phenomena. Regime-PCMCI is designed to uncover causal relationships in time-series where transitions between regimes exist, and different causal relationships may govern each regime. In this tutorial, we will first discuss how to frame the problem of understanding climate and weather extremes using regime-specific causal discovery. We will shortly introduce constraint-based causal discovery and present the Regime-PCMCI algorithm. To enable participants to gain hands-on experience with the algorithm, we will apply Regime-PCMCI, implemented in the open-source Python package Tigramite (https://github.com/jakobrunge/tigramite), to a real-world climate science problem. Our example will focus on validating hypothesized regime-specific causal graphs that describe the causal relationship between atmospheric circulation, temperature, rainfall, evaporation, and soil moisture under various moisture regimes. Our tutorial will cover essential steps such as data preprocessing, parameter selection, and interpretation of results, ensuring that all participants with a basic understanding of climate science or data analysis can grasp the presented concepts. With this tutorial, we wish to equip participants with the skills to apply Regime-PCMCI in their research to further uncover complex mechanisms in climate science, as this knowledge is crucial for more informed policy-making. Authors: Oana-Iuliana Popescu (Institute of Data Science, German Aerospace Center (DLR)); Wiebke Günther (German Aerospace Center); Raed Hamed (Institute for Environmental Studies, VU Amsterdam); Dominik Schumacher (4Institute for Atmospheric and Climate Science, ETH Zürich); Martin Rabel (DLR); Dim Coumou (IVM/VU); Jakob Runge (Institute of Data Science, German Aerospace Center (DLR))	Causal & Bayesian Methods Climate Science & Modeling Time-series Analysis
ICLR 2024	One Prompt Fits All: Visual Prompt-Tuning for Remote Sensing Segmentation (Tutorials Track) Audience Choice Abstract and authors: (click to expand) Abstract: Image segmentation is crucial in climate change research for analyzing satellite imagery. This technique is vital for ecosystems mapping, natural disasters assessment, and urban and agricultural planning. The advent of vision-based foundational models like the Segment Anything Model (SAM) opens new avenues in climate research and remote sensing (RS). SAM can perform segmentation tasks on any object from manually-crafted prompts. However, the efficacy of SAM largely depends on the quality of these prompts. This issue is particularly pronounced with RS data, which are inherently complex. To use SAM for accurate segmentation at scale for RS, one would need to create complex prompts for each image, which typically involves selecting dozens of points. To address this, we introduce Prompt-Tuned SAM (PT-SAM), a method that minimizes the need for manual input through a trainable, lightweight prompt embedding. This embedding captures key semantic information for specific objects of interest that would be applicable to unseen images. Our approach merges the zero-shot generalization capabilities of the pre-trained SAM model with supervised learning. Importantly, the training process for the prompt embedding not only has minimal hardware requirements, allowing it to be conducted on a CPU, but it also requires only a small dataset. With PT-SAM, image segmentation on RS data can be performed at scale without human intervention, achieving accuracies comparable to those of human-designed prompts with SAM. For example, PT-SAM can be used for analyzing forest cover across vast areas, a key factor in understanding the impact of human activities on forests. Its capability to segment a multitude of images makes it ideal for monitoring widespread land-cover changes, providing deeper insights into urbanization. This tutorial will explore how to train and utilize PT-SAM for large-scale segmentation tasks, specifically focusing on training embeddings that capture forests, and buildings. Authors: Marshall Wang (Vector Institute); John Willes (Vector Institute); Deval Pandya (Vector Institute)	Computer Vision & Remote Sensing Earth Observation & Monitoring Forests
ICLR 2024	1. Building Sustainable Futures: Tutorial on Carbon Footprint Analysis and Mitigation Strategies Using Counter Factual Queries (Tutorials Track) Abstract and authors: (click to expand) Abstract: As the sense of urgency regarding climate change continues to mount with growing regulatory pressure across the globe, it has become increasingly crucial for enterprises and governments to align their goals with sustainability values. They face a crucial imperative to act on climate change mitigation by disclosing their GHG emissions and committing to reduction and optimization of emissions from their industrial activities including operations, infrastructure, logistics, and supply chains. The world's largest enterprises have set long-term net-zero targets but lacks an integrated view of how their key business operations and processes contribute to their sustainability journey, which makes it difficult for them to embark on a well-planned journey to achieve their sustainability goals. With the recent advancement, AI intervention becomes imperative to measure, track, and improve ESG performance to achieve sustainability goals. This tutorial aims to provide a comprehensive guide on leveraging advanced AI techniques for analysing and mitigating carbon footprints in various sectors. The tutorial covers the utilization of a generalized framework that integrates sector-specific and cross-sector enterprise data, including assets and operations, to derive actionable insights. The framework also uses additional data such as weather parameters and contextual information to facilitate a holistic approach to carbon footprint analysis and its mitigation strategies. The tutorial will delve into the working of a framework which comprises of an LLM driven carbon accounting engine, predictive models for carbon emissions, anomaly detection models, and counterfactual models. It identifies the emission hotspots, thereafter provides actionable recommendations to mitigate the carbon emission. The proposed tutorial aims to empower participants with the knowledge and skills to make informed decisions towards building a more sustainable future Authors: Kumar Saurav (IBM); Manikandan Padmanaban (IBM Research India); Ayush Jain (IBM Research); Jagabondhu Hazra (IBM Research India)	Climate Science & Modeling Recommender Systems
NeurIPS 2023	Machine learning for gap-filling in greenhouse gas emissions databases (Papers Track) Abstract and authors: (click to expand) Abstract: Greenhouse Gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use 3 datasets of increasing complexity with 18 different gap-filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non-reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritisation to accelerate the improvement of datasets. Graph based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base ever more integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at https://hackmd.io/@luke-scot/ML-for-GHG-database-completion. Authors: Luke Cullen (University of Cambridge); Andrea Marinoni (UiT the Arctic University of Norway); Jonathan M Cullen (University of Cambridge)	Public Policy
NeurIPS 2023	EarthPT: a foundation model for Earth Observation (Papers Track) Abstract and authors: (click to expand) Abstract: We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. We demonstrate that EarthPT is an effective forecaster that can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 -> 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar ‘Large Observation Models.’ Authors: Michael J Smith (Aspia Space); Luke Fleming (Aspia Space); James Geach (Aspia Space)	Earth Observation & Monitoring
NeurIPS 2023	Towards Understanding Climate Change Perceptions: A Social Media Dataset (Papers Track) Abstract and authors: (click to expand) Abstract: Climate perceptions shared on social media are an invaluable barometer of public attention. By directing research towards this topic, we can eventually improve the effectiveness of climate change communication, increase public engagement, and enhance climate change education. We propose two real-world image datasets to promote impactful research both in the Computer Vision community and beyond. Firstly, ClimateTV, a dataset containing over 700,000 climate change-related images posted on Twitter and labelled on basis of the image hashtags. Secondly, ClimateCT, a Twitter dataset containing images with five-dimensional annotations in super-categories (i) Animals, (ii) Climate action, (iii) Consequences, (iv) Setting, and (v) Type. These challenging classification datasets contain classes which are designed according to their relevance in the context of climate change. The challenging nature of the datasets is given by varying class diversities (e.g. polar bear vs. land mammal) and foci (e.g. arctic vs. snowy residential area). The analyses of our datasets using CLIP embeddings and query optimization (CoCoOp) further showcase the challenging nature of ClimateTV and ClimateCT. Authors: Katharina Prasse (University of Siegen); Steffen Jung (MPII); Isaac B Bravo (Technische Universität München); Stefanie Walter (Technical University of Munich); Margret Keuper (University of Siegen, Max Planck Institute for Informatics)	Behavioral and Social Science
NeurIPS 2023	Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding (Papers Track) Abstract and authors: (click to expand) Abstract: Cloud analysis is a critical component of weather and climate science, impacting various sectors like disaster management. However, achieving fine-grained cloud analysis, such as cloud segmentation, in remote sensing remains challenging due to the inherent difficulties in obtaining accurate labels, leading to significant labeling errors in training data. Existing methods often assume the availability of reliable segmentation annotations, limiting their overall performance. To address this inherent limitation, we introduce an innovative model-agnostic Cloud Adaptive-Labeling (CAL) approach, which operates iteratively to enhance the quality of training data annotations and consequently improve the performance of the learned model. Our methodology commences by training a cloud segmentation model using the original annotations. Subsequently, it introduces a trainable pixel intensity threshold for adaptively labeling the cloud training images on-the-fly. The newly generated labels are then employed to fine-tune the model. Extensive experiments conducted on multiple standard cloud segmentation benchmarks demonstrate the effectiveness of our approach in significantly boosting the performance of existing segmentation models. Our CAL method establishes new state-of-the-art results when compared to a wide array of existing alternatives. Authors: Jay Gala (NMIMS); Sauradip Nag (University of Surrey); Huichou Huang (City University of Hong Kong); Ruirui Liu (Brunel University London); Xiatian Zhu (University of Surrey)	Climate Science & Modeling
NeurIPS 2023	Flamingo: Environmental Impact Factor Matching for Life Cycle Assessment with Zero-Shot ML (Papers Track) Abstract and authors: (click to expand) Abstract: Consumer products contribute to >75% of global greenhouse gas (GHG) emissions, primarily through indirect contributions from the supply chain. Measurement of GHG emissions associated with products is crucial to quantify the impact of GHG emission abatement actions. Life cycle assessment (LCA), the scientific discipline for measuring GHG emissions, estimates the environmental impact of a product. Scaling LCA to millions of products is challenging as it requires extensive manual analysis by domain experts. To avoid repetitive analysis, environmental impact factors (EIF) of common materials and products are published for use by experts. However, finding appropriate EIFs for even a single product can require hundreds of hours of manual work, especially for complex products. We present Flamingo, an algorithm that leverages neural language models to automatically identify an appropriate EIF given a text description. A key challenge in automation is that EIF databases are incomplete. Flamingo uses industry sector classification as an intermediate layer to identify when there are no good matches in the database. On a dataset of 664 products, Flamingo achieves an EIF matching precision of 75%. Authors: Bharathan Balaji (Amazon); Venkata Sai Gargeya Vunnava (amazon); Nina Domingo (Amazon); Shikhar Gupta (Amazon); Harsh Gupta (Amazon); Geoffrey Guest (Amazon); Aravind Srinivasan (Amazon); Kellen Axten (Amazon); Jared Kramer (Amazon)	Natural Language Processing
NeurIPS 2023	AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning (Papers Track) Abstract and authors: (click to expand) Abstract: AtmoRep is a novel, task-independent stochastic computer model of atmospheric dynamics inspired by the concept of foundation models in natural language processing, like the GPT line or PalmX, applied in the context of Earth system science. The main innovative aspect consists in the fact that the model can skillfully solve scientific tasks it was not specifically trained on, clearly exhibiting in-context learning capabilities. AtmoRep's skill has been tested on nowcasting, temporal interpolation, model correction, and counterfactuals, demonstrating that large-scale neural networks can provide skillful, task-independent models able to complement the existing numerical approaches in multiple applications. In addition, the authors also demonstrated the possibility to further increase the model accuracy by fine tuning it directly on observational data for tasks such as precipitation corrections or downscaling. Authors: ilaria luise (CERN)	Climate Science & Modeling
NeurIPS 2023	Artificial Intelligence for Methane Mitigation : Through an Automated Determination of Oil and Gas Methane Emissions Profiles (Papers Track) Abstract and authors: (click to expand) Abstract: The oil and gas sector is the second largest anthropogenic emitter of methane, which is responsible for approximately 25% of global warming since pre-industrial times. In order to mitigate methane atmospheric emissions from oil and gas industry, the potential emitting infrastructure must be monitored. Initiatives such as the Methane Alert and Response System (MARS), launched by the United Nations Environment Program, aim to locate significant emissions events, alert relevant stakeholders, as well as monitor and track progress in mitigation efforts. To achieve this goal, an automated solution is needed for consistent monitoring across multiple oil and gas basins around the world. Most methane emissions analysis studies propose post-emission analysis. The works and future guidelines presented in this paper aim to provide an automated collection of informed methane emissions by oil and gas site and infrastructure which are necessary to dress emission profile in near real time. This proposed framework also permits to create action margins to reduce methane emissions by passing from post methane emissions analysis to forecasting methods. Authors: Jade Eva Guisiano (Sorbonne / ISEP / Polytechnique / UNEP); Thomas LAUVAUX (Université de Reims); Eric Moulines (Ecole Polytechnique); Jérémie Sublime (ISEP)	Earth Observation & Monitoring
NeurIPS 2023	Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation (Papers Track) Abstract and authors: (click to expand) Abstract: Fusing abundant satellite data with sparse ground measurements constitutes a major challenge in climate modeling. To address this, we propose a strategy to augment the training dataset by introducing unlabeled satellite images paired with pseudo-labels generated through a spatial interpolation technique known as ordinary kriging, thereby making full use of the available satellite data resources. We show that the proposed data augmentation strategy helps enhance the performance of the state-of-the-art convolutional neural network-random forest (CNN-RF) model by a reasonable amount, resulting in a noteworthy improvement in spatial correlation and a reduction in prediction error. Authors: lei Duan (Duke University); Ziyang Jiang (Duke University); David Carlson (Duke University)	Computer Vision & Remote Sensing
NeurIPS 2023	Physics-Informed Domain-Aware Atmospheric Radiative Transfer Emulator for All Sky Conditions (Papers Track) Abstract and authors: (click to expand) Abstract: Radiative transfer modeling is a complex and computationally expensive process that is used to predict how radiation interacts with the atmosphere and Earth's surface. The Rapid Radiation Transfer Model (RRTM) is one such process model that is used in many Earth system models. In recent years, there has been a growing interest in using machine learning (ML) to speed up radiative transfer modeling. ML algorithms can be trained on large datasets of existing RRTM simulations to learn how to predict the results of new simulations without having to run the full RRTM model so one can use the algorithm for new simulations with very light computational demand. This study developed a new physics-based ML emulator for RRTM that is built on a convolutional neural network (CNN) where we trained the CNN on a dataset of 28 years of RRTM simulations. We built a custom loss function, which incorporates information on how radiation interacts with clouds at day- and night-time. The emulator was able to learn how to predict the vertical heating rates in the atmosphere with a high degree of accuracy (RMSE of less than 2% and Pearson's correlation above 0.9). The new ML emulator is over 56 times faster than the original RRTM model on traditional multi-CPU machines. This speedup could allow scientists to call the RRTM much more frequently in atmosphere models, which may improve the accuracy of climate models and reduce the uncertainty in the future climate projections. Authors: Piyush Garg (Argonne National Laboratory); Emil Constantinescu (Argonne National Laboratory); Bethany Lusch (Argonne National Lab); Troy Arcomano (Argonne National Laboratory); Jiali Wang (Argonne National Laboratory); Rao Kotamarthi (Argonne National Laboratory)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023	Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems? (Papers Track) Abstract and authors: (click to expand) Abstract: Photovoltaic (PV) energy is crucial for the decarbonization of energy systems. Due to the lack of centralized data, remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. However, current techniques lack reliability and are notably sensitive to shifts in the acquisition conditions. To overcome this, we leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions, thus increasing trust in deep learning systems to encourage their use for the safe integration of clean energy in electric systems. Authors: Gabriel Kasmi (Mines Paris - PSL); Laurent Dubus (RTE France); Yves-Marie Saint-Drenan (Mines Paris - PSL); Philippe Blanc (Mines Paris - PSL)	Computer Vision & Remote Sensing
NeurIPS 2023	Uncertainty Quantification of the Madden–Julian Oscillation with Gaussian Processes (Papers Track) Abstract and authors: (click to expand) Abstract: The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. To address this problem, we develop a nonparametric strategy based on Gaussian process (GP) models. We calibrate GPs using empirical correlations. Furthermore, we propose a posteriori covariance correction that extends the probabilistic coverage by more than three weeks. Authors: Haoyuan Chen (Texas A&M University); Emil Constantinescu (Argonne National Laboratory); Vishwas Rao (Argonne National Laboratory); Cristiana Stan (George Mason University)	Climate Science & Modeling Uncertainty Quantification & Robustness
NeurIPS 2023	Ocean Wave Energy: Optimizing Reinforcement Learning Agents for Effective Deployment (Papers Track) Abstract and authors: (click to expand) Abstract: Fossil fuel energy production is a leading cause of climate change. While wind and solar energy have made advancements, ocean waves, a more consistent clean energy source, remain underutilized. Wave Energy Converters (WEC) transform wave power into electric energy. To be economically viable, modern WECs need sophisticated real-time controllers that boost energy output and minimize mechanical stress, thus lowering the overall cost of energy (LCOE). This paper presents how a Reinforcement Learning (RL) controller can outperform the default spring damper controller for complex spread waves in the sea, enhancing wave energy's viability. Using the Proximal Policy Optimization (PPO) algorithm with Transformer variants as function approximators, the RL controllers optimize multi-generator Wave Energy Converters (WEC), leveraging wave sensor data for multiple cost-efficiency goals. After successful tests in the EuropeWave\footnote{EuropeWave: https://www.europewave.eu/} project's emulator tank, the platform is planned to deploy. We discuss the challenges of deployment at the BiMEP site and how we had to tune the RL controller to address that. The RL controller outperforms the default Spring Damper controller in the BiMEP\footnote{BiMEP: https://www.bimep.com/en/} conditions by 22.8% on energy capture. Enhancing wave energy's economic viability will expedite the transition to clean energy, reducing carbon emissions and fostering a healthier climate. Authors: Vineet Gundecha (Hewlett Packard Enterpise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Avisek Naug (Hewlett Packard Enterprise); Alexandre Pichard (Carnegie Clean Energy); mathieu Cocho (Carnegie Clean Energy); Soumyendu Sarkar (Hewlett Packard Enterprise)	Power & Energy Reinforcement Learning
NeurIPS 2023	Sustainable Data Center Modeling: A Multi-Agent Reinforcement Learning Benchmark (Papers Track) Abstract and authors: (click to expand) Abstract: The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent control of DC components such as cooling, load shifting, and energy storage is essential. However, the complexity of managing these controls in tandem with external factors like weather and green energy availability presents a significant challenge. While some individual components like HVAC control have seen research in Reinforcement Learning (RL), there's a gap in holistic optimization covering all elements simultaneously. To tackle this, we've developed DCRL, a multi-agent RL environment that empowers the ML community to research, develop, and refine RL controllers for carbon footprint reduction in DCs. DCRL is a flexible, modular, scalable, and configurable platform that can handle large High Performance Computing (HPC) clusters. In its default setup, DCRL also provides a benchmark for evaluating multi-agent RL algorithms, facilitating collaboration and progress in green computing research. Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Avisek Naug (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Sajad Mousavi (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs)	Buildings Reinforcement Learning
NeurIPS 2023	ACE: A fast, skillful learned global atmospheric model for climate prediction (Papers Track) Abstract and authors: (click to expand) Abstract: Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture. The emulator is stable for 100 years, nearly conserves column moisture without explicit constraints and faithfully reproduces the reference model's climate, outperforming a challenging baseline on over 90% of tracked variables. ACE requires nearly 100x less wall clock time and is 100x more energy efficient than the reference model using typically available resources. Without fine-tuning, ACE can stably generalize to a previously unseen historical sea surface temperature dataset. Authors: Oliver Watt-Meyer (Allen Institute for AI); Gideon Dresdner (Allen Institute for AI Climate Science); Jeremy McGibbon (Allen Institute for AI); Spencer K Clark (Allen Institute for Artificial Intelligence); James Duncan (University of California, Berkeley); Brian Henn (Allen Institute for AI); Matthew Peters (AI2); Noah D Brenowitz (NVIDIA); Karthik Kashinath (NVIDIA); Mike Pritchard (NVIDIA); Boris Bonev (NVIDIA); Christopher Bretherton (Allen Institute for AI)	Climate Science & Modeling
NeurIPS 2023	A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration (Papers Track) Abstract and authors: (click to expand) Abstract: There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data centers have an important impact on data center carbon footprint. In this paper, we showcase PyDCM, a Python library that enables extremely fast prototyping of data center design and applies reinforcement learning-enabled control with the purpose of evaluating key sustainability metrics, including carbon footprint, energy consumption, and observing temperature hotspots. We demonstrate these capabilities of PyDCM and compare them to existing works in EnergyPlus for modeling data centers. PyDCM can also be used as a standalone Gymnasium environment for demonstrating sustainability-focused data center control. Authors: Avisek Naug (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Sajad Mousavi (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Soumyendu Sarkar (Hewlett Packard Enterprise)	Buildings Reinforcement Learning
NeurIPS 2023	Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean (Papers Track) Abstract and authors: (click to expand) Abstract: Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprint and roof classification maps. By enhancing local capacity in government agencies, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean. Authors: Isabelle Tingzon (The World Bank); Nuala Margaret Cowan (The World Bank); Pierre Chrzanowski (The World Bank)	Disaster Management and Relief
NeurIPS 2023	Graph-based Neural Weather Prediction for Limited Area Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming increasingly vital. While most existing Neural Weather Prediction (NeurWP) methods focus on global forecasting, an important question is how these techniques can be applied to limited area modeling. In this work we adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region. Authors: Joel Oskarsson (Linköping University); Tomas Landelius (SMHI); Fredrik Lindsten (Linköping University)	Climate Science & Modeling Time-series Analysis
NeurIPS 2023	Self-supervised Pre-training for Precipitation Post-processor (Papers Track) Abstract and authors: (click to expand) Abstract: Securing sufficient forecast lead time for local precipitation is essential for preventing hazardous weather events. Nonetheless, global warming-induced climate change is adding to the challenge of accurately predicting severe precipitation events, such as heavy rainfall. In this work, we propose a deep learning-based precipitation post-processor approach to numerical weather prediction (NWP) models. The precipitation post-processor consists of (i) self-supervised pre-training, where parameters of encoder are pre-trained on the reconstruction of masked variables of the atmospheric physics domain, and (ii) transfer learning on precipitation segmentation tasks (target domain) from the pre-trained encoder. We also introduce a heuristic labeling approach for effectively training class-imbalanced datasets. Our experiment results in precipitation correction for regional NWP show that the proposed method outperforms other approaches. Authors: Sojung An (Korea Institute of Atmosphere Prediction Systems); Junha Lee (Korea Institute of Industrial Technology); Jiyeon Jang (Korea Institute of Atmosphere Prediction Systems); Inchae Na (Korea Institute of Atmosphere Prediction Systems); Wooyeon Park (Korea Institute of Atmosphere Prediction Systems); Sujeong You (KITECH)	Extreme Weather
NeurIPS 2023	Enhancing Data Center Sustainability with a 3D CNN-Based CFD Surrogate Model (Papers Track) Abstract and authors: (click to expand) Abstract: Thermal Computational Fluid Dynamics (CFD) models analyze airflow and heat distribution in data centers, but their complex computations hinder efficient energy-saving optimizations for sustainability. We introduce a new method to acquire data and model 3D Convolutional Neural Network (CNN) based surrogates for CFDs, which predict a data center's temperature distribution based on server workload, HVAC airflow rate, and temperature set points. The surrogate model's predictions are highly accurate, with a mean absolute error of 0.31°C compared to CFD-based ground truth temperatures. The surrogate model is three orders of magnitude faster than CFDs in generating the temperature maps for similar-sized data centers, enabling real-time applications. It helps to quickly identify and reduce temperature hot spots($7.7%) by redistributing workloads and saving cooling energy($2.5%). It also aids in optimizing server placement during installation, preventing issues, and increasing equipment lifespan. These optimizations boost sustainability by reducing energy use, improving server performance, and lowering environmental impact. Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Zachariah Carmichael (University of Notre Dame); Vineet Gundecha (Hewlett Packard Enterpise); Avisek Naug (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Ricardo Luna Gutierrez (Hewlett Packard Enterprise)	Buildings Computer Vision & Remote Sensing
NeurIPS 2023	Price-Aware Deep Learning for Electricity Markets (Papers Track) Abstract and authors: (click to expand) Abstract: While deep learning gradually penetrates operational planning of power systems, its inherent prediction errors may significantly affect electricity prices. This paper examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing. Authors: Vladimir Dvorkin (Massachusetts Institute of Technology); Ferdinando Fioretto (University of Virginia)	Power & Energy
NeurIPS 2023	A machine learning framework for correcting under-resolved simulations of turbulent systems using nudged datasets (Papers Track) Abstract and authors: (click to expand) Abstract: Due to the rapidly changing climate, the frequency and severity of extreme weather, such as storms and heatwaves is expected to increase drastically over the coming decades. Accurately quantifying the risk of such events with high spatial resolution is a critical step in the implementation of strategies to prepare for and mitigate the damages. As fully resolved simulations remain computationally out of reach, policy makers must rely on coarse resolution climate models which either parameterize or completely ignore sub-grid scale dynamics. In this work we propose a machine learning framework to debias under-resolved simulations of complex and chaotic dynamical systems such as atmospheric dynamics. The proposed strategy uses ``nudged'' simulations of the coarse model to generate training data designed to minimize the effects of chaotic divergence. We illustrate through a prototype QG model that the proposed approach allows us to machine learn a map from the chaotic attractor of under-resolved dynamics to that of the fully resolved system. In this way we are able to recover extreme event statistics using a very small training dataset. Authors: Benedikt Barthel (MIT); Themis Sapsis (MIT)	Climate Science & Modeling
NeurIPS 2023	Can Deep Learning help to forecast deforestation in the Amazonian Rainforest? (Papers Track) Abstract and authors: (click to expand) Abstract: Deforestation is a major driver of climate change. To mitigate deforestation, carbon offset projects aim to protect forest areas at risk. However, existing literature shows that most projects have substantially overestimated the risk of deforestation, thereby issuing carbon credits without equivalent emissions reductions. In this study, we examine if the spread of deforestation can be predicted ex-ante using Deep Learning (DL) models. Our input data includes past deforestation development, slope information, land use, and other terrain- and soil-specific covariates. Testing predictions 1-year ahead, we find that our models only achieve low levels of predictability. For pixel-wise classification at a 30 m resolution, our models achieve an F1 score of 0.263. Only when substantially simplifying the task to predicting if any level of deforestation occurs within a 1.5 km squared tile, the model results improve to a moderate performance (F1: 0.608). We conclude that, based on our input data, deforestation cannot be predicted accurately enough to justify the ex-ante issuance of carbon credits for forest conservation projects. As main challenges, there is the extreme class imbalance between pixels that are deforested (minority) and not deforested (majority) as well as the omittance of social, political, and economic drivers of deforestation. Authors: Tim Engelmann (ETH Zurich); Malte Toetzke (ETH Zurich)	Forests Time-series Analysis
NeurIPS 2023	Spatially-resolved emulation of climate extremes via machine learning stochastic models (Papers Track) Abstract and authors: (click to expand) Abstract: Emulators, or reduced-complexity models, serve as an ideal complement to earth system models (ESM) by providing the climate information under various scenarios at much lower computational costs. We develop an emulator of climate extremes that produce the temporal evolution of probability distributions of local variables on a spatially resolved grid. The representative modes of climate change are identified using principal component analysis (PCA), and the PCA time series are approximated using stochastic models. When applied to ERA5 data, the model accurately reproduces the quantiles of local daily maximum temperature and effectively captures the non-Gaussian statistics. We also discuss potential generalization of our emulator to different climate change scenarios. Authors: Mengze Wang (Massachusetts Institute of Technology); Andre Souza (Massachusetts Institute of Technology); Raffaele Ferrari (Massachusetts Institute of Technology); Themis Sapsis (MIT)	Climate Science & Modeling
NeurIPS 2023	Reinforcement Learning control for Airborne Wind Energy production (Papers Track) Abstract and authors: (click to expand) Abstract: Airborne Wind Energy (AWE) is an emerging technology that promises to be able to harvest energy from strong high-altitude winds, while addressing some of the key critical issues of current wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station, fly driven by the wind and convert the mechanical energy of wind into electrical energy by means of a generator. Such systems are usually controlled by adjusting the trajectory of the kite using optimal control techniques, such as model-predictive control. These methods are based upon a mathematical model of the system to control, and they produce results that are strongly dependent on the specific model at use and difficult to generalize. Our aim is to replace these classical techniques with an approach based on Reinforcement Learning (RL), which can be used even in absence of a known model. Experimental results prove that RL is a viable method to control AWE systems in complex simulated environments, including turbulent flows. Authors: Lorenzo Basile (University of Trieste); Maria Grazia Berni (University of Trieste); Antonio Celani (ICTP)	Power & Energy Reinforcement Learning
NeurIPS 2023	Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence (Papers Track) Abstract and authors: (click to expand) Abstract: Global climate models (GCMs) are the main tools for understanding and predicting climate change. However, due to limited numerical resolutions, these models suffer from major structural uncertainties; e.g., they cannot resolve critical processes such as small-scale eddies in atmospheric and oceanic turbulence. Thus, such small-scale processes have to be represented as a function of the resolved scales via closures (parametrization). The accuracy of these closures is particularly important for capturing climate extremes. Traditionally, such closures are based on heuristics and simplifying assumptions about the unresolved physics. Recently, supervised-learned closures, trained offline on high-fidelity data, have been shown to outperform the classical physics-based closures. However, this approach requires a significant amount of high-fidelity training data and can also lead to instabilities. Reinforcement learning is emerging as a potent alternative for developing such closures as it requires only low-order statistics and leads to stable closures. In Scientific Multi-Agent Reinforcement Learning (SMARL) computational elements serve a dual role of discretization points and learning agents. Here, we leverage SMARL and fundamentals of turbulence physics to learn closures for canonical prototypes of atmospheric and oceanic turbulence. The policy is trained using only the enstrophy spectrum, which is nearly invariant and can be estimated from a few high-fidelity samples. We show that these closures lead to stable low-resolution simulations that, at a fraction of the cost, can reproduce the high-fidelity simulations' statistics, including the tails of the probability density functions (PDFs). These results demonstrate the high potential of SMARL for closure modeling for GCMs, especially in the regime of scarce data and indirect observations. Authors: Rambod Mojgani (Rice University); Daniel Waelchli (ETHZ); Yifei Guan (Rice University); Petros Koumoutsakos (Harvard); Pedram Hassanzadeh (Rice University)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023	Machine learning derived sub-seasonal to seasonal extremes (Papers Track) Abstract and authors: (click to expand) Abstract: Improving the accuracy of sub-seasonal to seasonal (S2S) extremes can significantly impact society. Providing S2S forecasts in risk or extreme indices can aid disaster response, especially for drought and flood events. Additionally, it can provide updates on disease outbreaks and aid in predicting the occurrence, duration, and decline of heat waves. This work uses a transformer model to predict the daily temperature distributions in the S2S scale. We analyze how the model performs in extreme temperatures by comparing its output distributions with those obtained from ECMWF forecasts across different metrics. Our model produces better responses for temperatures in average and extreme regions. Also, we show how our model better captures the heatwave that hit Europe in the summer of 2019. Authors: Daniel Salles Civitarese (IBM Research, Brazil); Bianca Zadrozny (IBM Research)	Time-series Analysis Extreme Weather
NeurIPS 2023	Understanding Opinions Towards Climate Change on Social Media (Papers Track) Abstract and authors: (click to expand) Abstract: Social media platforms such as Twitter (now known as X) have revolutionized how the public engage with important societal and political topics. Recently, climate change discussions on social media became a catalyst for political polarization and the spreading of misinformation. In this work, we aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media. To this end, we extracted and analyzed a dataset of 13.6 millions tweets sent by 3.6 million users from 2006 to 2019. Then, we construct a temporal graph from the user-user mentions network and utilize the Louvain community detection algorithm to analyze the changes in community structure around Conference of the Parties on Climate Change (COP) events. Next, we also apply tools from the Natural Language Processing literature to perform sentiment analysis and topic modeling on the tweets. Our work acts as a first step towards understanding the evolution of pro-climate change communities around COP events. Answering these questions helps us understand how to raise people's awareness towards climate change thus hopefully calling on more individuals to join the collaborative effort in slowing down climate change. Authors: Yashaswi Pupneja (University of Montreal); Yuesong Zou (McGill University); Sacha Levy (Yale University); Shenyang Huang (Mila/McGill University)	Behavioral and Social Science Data Mining
NeurIPS 2023	Real-time Carbon Footprint Minimization in Sustainable Data Centers wth Reinforcement Learning (Papers Track) Best ML Innovation Abstract and authors: (click to expand) Abstract: As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. There is a pressing need to optimize energy usage in these centers, especially considering factors like cooling, balancing flexible load based on renewable energy availability, and battery storage utilization. The challenge arises due to the interdependencies of these strategies with fluctuating external factors such as weather and grid carbon intensity. Although there's currently no real-time solution that addresses all these aspects, our proposed Data Center Carbon Footprint Reduction (DC-CFR) framework, based on multi-agent Reinforcement Learning (MARL), targets carbon footprint reduction, energy optimization, and cost. Our findings reveal that DC-CFR's MARL agents efficiently navigate these complexities, optimizing the key metrics in real-time. DC-CFR reduced carbon emissions, energy consumption, and energy costs by over 13% with EnergyPlus simulation compared to the industry standard ASHRAE controller controlling HVAC for a year in various regions. Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Avisek Naug (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Cullen Bash (HPE)	Buildings Reinforcement Learning
NeurIPS 2023	Machine learning applications for weather and climate predictions need greater focus on extremes (Papers Track) Abstract and authors: (click to expand) Abstract: Multiple studies have now demonstrated that machine learning (ML) can give improved skill for predicting or simulating fairly typical weather events, for tasks such as short-term and seasonal weather forecasting, downscaling simulations to higher resolution and emulating and speeding up expensive model parameterisations. Many of these used ML methods with very high numbers of parameters, such as neural networks, which are the focus of the discussion here. Not much attention has been given to the performance of these methods for extreme event severities of relevance for many critical weather and climate prediction applications, with return periods of more than a few years. This leaves a lot of uncertainty about the usefulness of these methods, particularly for general purpose prediction systems that must perform reliably in extreme situations. ML models may be expected to struggle to predict extremes due to there usually being few samples of such events. However, there are some studies that do indicate that ML models can have reasonable skill for extreme weather, and that it is not hopeless to use them in situations requiring extrapolation. This paper reviews these studies, updating an earlier review, and argues that this is an area that needs researching more. Ways to get a better understanding of how well ML models perform at predicting extreme weather events are discussed. Authors: Peter Watson (Bristol)	Climate Science & Modeling Extreme Weather
NeurIPS 2023	Climate-sensitive Urban Planning through Optimization of Tree Placements (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is increasing the intensity and frequency of many extreme weather events, including heatwaves, which results in increased thermal discomfort and mortality rates. While global mitigation action is undoubtedly necessary, so is climate adaptation, e.g., through climate-sensitive urban planning. Among the most promising strategies is harnessing the benefits of urban trees in shading and cooling pedestrian-level environments. Our work investigates the challenge of optimal placement of such trees. Physical simulations can estimate the radiative and thermal impact of trees on human thermal comfort but induce high computational costs. This rules out optimization of tree placements over large areas and considering effects over longer time scales. Hence, we employ neural networks to simulate the point-wise mean radiant temperatures--a driving factor of outdoor human thermal comfort--across various time scales, spanning from daily variations to extended time scales of heatwave events and even decades. To optimize tree placements, we harness the innate local effect of trees within the iterated local search framework with tailored adaptations. We show the efficacy of our approach across a wide spectrum of study areas and time scales. We believe that our approach is a step towards empowering decision-makers, urban designers and planners to proactively and effectively assess the potential of urban trees to mitigate heat stress. Authors: Simon Schrodi (University of Freiburg); Ferdinand Briegel (University of Freiburg); Max J. Argus (University Of Freiburg); Andreas Christen (University of Freiburg); Thomas Brox (University of Freiburg)	Cities & Urban Planning
NeurIPS 2023	Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change has resulted in a year over year increase in adverse weather and weather conditions which contribute to increasingly severe fire seasons. Without effective mitigation, these fires pose a threat to life, property, ecology, cultural heritage, and critical infrastructure. To better prepare for and react to the increasing threat of wildfires, more accurate fire modelers and mitigation responses are necessary. In this paper, we introduce SimFire, a versatile wildland fire projection simulator designed to generate realistic wildfire scenarios, and SimHarness, a modular agent-based machine learning wrapper capable of automatically generating land management strategies within SimFire to reduce the overall damage to the area. Together, this publicly available system allows researchers and practitioners the ability to emulate and assess the effectiveness of firefighter interventions and formulate strategic plans that prioritize value preservation and resource allocation optimization. The repositories are available for download at https://github.com/mitrefireline. Authors: Alexander Tapley (The MITRE Corporation); savanna o smith (MITRE); Tim Welsh (The MITRE Corporation); Aidan Fennelly (The MITRE Corporation); Dhanuj M Gandikota (The MITRE Corporation); Marissa Dotter (MITRE Corporation); Michael Doyle (The MITRE Corporation); Michael Threet (MITRE)	Reinforcement Learning Disaster Management and Relief
NeurIPS 2023	Combining deep generative models with extreme value theory for synthetic hazard simulation: a multivariate and spatially coherent approach (Papers Track) Abstract and authors: (click to expand) Abstract: Climate hazards can cause major disasters when they occur simultaneously as compound hazards. To understand the distribution of climate risk and inform adaptation policies, scientists need to simulate a large number of physically realistic and spatially coherent events. Current methods are limited by computational constraints and the probabilistic spatial distribution of compound events is not given sufficient attention. The bottleneck in current approaches lies in modelling the dependence structure between variables, as inference on parametric models suffers from the curse of dimensionality. Generative adversarial networks (GANs) are well-suited to such a problem due to their ability to implicitly learn the distribution of data in high-dimensional settings. We employ a GAN to model the dependence structure for daily maximum wind speed, significant wave height, and total precipitation over the Bay of Bengal, combining this with traditional extreme value theory for controlled extrapolation of the tails. Once trained, the model can be used to efficiently generate thousands of realistic compound hazard events, which can inform climate risk assessments for climate adaptation and disaster preparedness. The method developed is flexible and transferable to other multivariate and spatial climate datasets. Authors: Alison M Peard (University of Oxford); Jim Hall (University of Oxford)	Societal Adaptation & Resilience Generative Modeling
NeurIPS 2023	Uncertainty Quantified Machine Learning for Street Level Flooding Predictions in Norfolk, Virginia (Papers Track) Abstract and authors: (click to expand) Abstract: Everyday citizens, emergency responders, and critical infrastructure can be dramatically affected by the flooding of streets and roads. Climate change exacerbates these floods through sea level rise and more frequent major storm events. Low-level flooding, such as nuisance flooding, continues to increase in frequency, especially in cities like Norfolk, Virginia, which can expect nearly 200 flooding events by 2050 [1]. Recently, machine learning (ML) models have been leveraged to produce real-time predictions based on local weather and geographic conditions. However, ML models are known to produce unusual results when presented with data that varies from their training set. For decision-makers to determine the trustworthiness of the model's predictions, ML models need to quantify their prediction uncertainty. This study applies Deep Quantile Regression to a previously published, Long Short-Term Memory-based model for hourly water depth predictions [2], and analyzes its out-of-distribution performance. Authors: Steven Goldenberg (Thomas Jefferson National Accelerator Facility); Diana McSpadden (Thomas Jefferson National Accelerator Facility); Binata Roy (University of Virginia); Malachi Schram (Thomas Jefferson National Accelerator Facility); Jonathan Goodall (University of Virginia); Heather Richter (Old Dominion University)	Climate Science & Modeling Uncertainty Quantification & Robustness
NeurIPS 2023	Weakly-semi-supervised object detection in remotely sensed imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models often require large datasets labeled with bounding box annotations which are expensive to curate, prohibiting the development of models for new tasks and geographies. To address this challenge, we develop weakly-semi-supervised object detection (WSSOD) models on remotely sensed imagery which can leverage a small amount of bounding boxes together with a large amount of point labels that are easy to acquire at scale in geospatial data. We train WSSOD models which use large amounts of point-labeled images with varying fractions of bounding box labeled images in FAIR1M and a wind turbine detection dataset, and demonstrate that they substantially outperform fully supervised models trained with the same amount of bounding box labeled images on both datasets. Furthermore, we find that the WSSOD models trained with 2-10x fewer bounding box labeled images can perform similarly to or outperform fully supervised models trained on the full set of bounding-box labeled images. We believe that the approach can be extended to other remote sensing tasks to reduce reliance on bounding box labels and increase development of models for impactful applications. Authors: Ji Hun Wang (Stanford University); Jeremy Irvin (Stanford University); Beri Kohen Behar (Stanford University); Ha Tran (Stanford University); Raghav Samavedam (Stanford University); Quentin Hsu (Stanford University); Andrew Ng (Stanford University)	Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2023	Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors (Papers Track) Abstract and authors: (click to expand) Abstract: While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for them to be economical. A significant bottleneck towards applying more advanced control algorithms is the need for better plasma simulation, where both physics-based and data-driven approaches currently fall short. The former is bottle-necked by both computational cost and the difficulty of modelling plasmas, and the latter is bottle-necked by the relative paucity of data. To address this issue, this work applies the neural ordinary differential equations (ODE) framework to the problem of predicting a subset of plasma dynamics, namely the coupled plasma current and internal inductance dynamics. As the neural ODE framework allows for the natural inclusion of physics-based inductive biases, we train both physics-based and neural network models on data from the Alcator C-Mod fusion reactor and find that a model that combines physics-based equations with a neural ODE performs better than both existing physics-motivated ODEs and a pure neural ODE model. Authors: Allen Wang (MIT); Cristina Rea (MIT); Darren Garnier (MIT)	Hybrid Physical Models Reinforcement Learning
NeurIPS 2023	Explainable Offline-online Training of Neural Networks for Multi-scale Climate Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: In global climate models, small-scale physical processes are represented using subgrid-scale (SGS) models known as parameterizations, and these parameterizations contribute substantially to uncertainties in climate projections. Recently, machine learning techniques, particularly deep neural networks (NNs), have emerged as novel tools for developing SGS parameterizations. Different strategies exist for training these NN-based SGS models. Here, we use a 1D model of the quasi-biennial oscillation (QBO) and atmospheric gravity wave (GW) parameterizations as testbeds to explore various learning strategies and challenges due to scarcity of high-fidelity training data. We show that a 12-layer convolutional NN that predicts GW forcings for given wind profiles, when trained offline in a big-data regime (100-years), produces realistic QBOs once coupled to the 1D model. In contrast, offline training of this NN in a small-data regime (18-months) yields unrealistic QBOs. However, online re-training of just two layers of this NN using ensemble Kalman inversion and only time-averaged QBO statistics leads to parameterizations that yield realistic QBOs. Fourier analysis of these three NNs’ kernels suggests how/why re-training works and reveals that these NNs primarily learn low-pass, high-pass, and a combination of band-pass Gabor filters, consistent with the importance of both local and non-local dynamics in GW propagation/dissipation. These strategies/findings apply to data-driven parameterizations of other climate processes generally. Authors: Hamid Alizadeh Pahlavan (Rice University); Pedram Hassanzadeh (Rice University); M. Joan Alexander (NorthWest Research Associates)	Hybrid Physical Models Unsupervised & Semi-Supervised Learning
NeurIPS 2023	Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of characterizing and tracking changes in ocean physics in complex models. Using the Antarctic Circumpolar Current as a case study, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models. To this end, we cluster grid cells into dynamical regimes and train an ensemble of neural networks, allowing uncertainty quantification, to predict these regimes and track them under climate change. Finally, we leverage this new knowledge to elucidate the dynamical drivers of the identified regime shifts as noted by the neural network using the `explainability' methods SHAP and Layer-wise Relevance Propagation. A region undergoing a profound shift is where the Antarctic Circumpolar Current intersects the Pacific-Antarctic Ridge, an area important for carbon draw-down and fisheries. In this region, THOR specifically reveals a shift in dynamical regime under climate change driven by changes in wind stress and interactions with bathymetry. Using this knowledge to guide further exploration, we find that as the Antarctic Circumpolar Current shifts north under intensifying wind stress, the dominant dynamical role of bathymetry weakens and the flow intensifies. Authors: William J Yik (Harvey Mudd College); Maike Sonnewald (University of California, Davis); Mariana Clare (ECMWF); Redouane Lguensat (IPSL)	Oceans & Marine Systems Interpretable ML
NeurIPS 2023	Prototype-oriented Unsupervised Change Detection for Disaster Management (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the remote sensing community has explored change detection methods. These methods are primarily categorized into supervised techniques, which yield precise results but come with high labeling costs, and unsupervised techniques, which eliminate the need for labeling but involve intricate hyperparameter tuning. To address these challenges, we propose a novel unsupervised change detection method named Prototype-oriented Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures changes by comparing features from pre-event, post-event, and prototype-oriented change synthesis images via a foundational model, and refines results using the Segment Anything Model (SAM). Although PUCD is an unsupervised change detection, it does not require complex hyperparameter tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the disaster dataset and it achieves state-of-the-art performance compared to other methods on the LEVIR-Extension dataset. Authors: YoungTack Oh (SI Analytics); Minseok Seo (si-analytics); Doyi Kim (SI Analytics); Junghoon Seo (SI Analytics)	Disaster Management and Relief Earth Observation & Monitoring
NeurIPS 2023	How to Recycle: General Vision-Language Model without Task Tuning for Predicting Object Recyclability (Papers Track) Abstract and authors: (click to expand) Abstract: Waste segregation and recycling place a crucial role in fostering environmental sustainability. However, discerning the whether a material is recyclable or not poses a formidable challenge, primarily because of inadequate recycling guidelines to accommodate a diverse spectrum of objects and their varying conditions. We investigated the role of vision-language models in addressing this challenge. We curated a dataset consisting >1000 images across 11 disposal categories for optimal discarding and assessed the applicability of general vision-language models for recyclability classification. Our results show that Contrastive Language-Image Pre- training (CLIP) model, which is pretrained to understand the relationship between images and text, demonstrated remarkable performance in the zero-shot recycla- bility classification task, with an accuracy of 89%. Our results underscore the potential of general vision-language models in addressing real-world challenges, such as automated waste sorting, by harnessing the inherent associations between visual and textual information. Authors: Eliot Park (Harvard Medical School); Eddy Pan (Harvard Medical School); Shreya Johri (Harvard Medical School); Pranav Rajpurkar (Harvard Medical School)	Sustainability Natural Language Processing
NeurIPS 2023	Flowering Onset Detection: Traditional Learning vs. Deep Learning Performance in a Sparse Label Context (Papers Track) Abstract and authors: (click to expand) Abstract: Detecting temporal shifts in plant flowering times is of increasing importance in a context of climate change, with applications in plant ecology, but also health, agriculture, and ecosystem management. However, scaling up plant-level monitoring is cost prohibitive, and flowering transitions are complex and difficult to model. We develop two sets of approaches to detect the onset of flowering at large-scale and high-resolution. Using fine grain temperature data with domain knowledge based features and traditional machine learning models provides the best performance. Using satellite data, with deep learning to deal with high dimensionality and transfer learning to overcome ground truth label sparsity, is a challenging but promising approach, as it reaches good performance with more systematically available data. Authors: Mauricio Soroco (University of British Columbia); Joel Hempel (University of British Columbia); Xinze Xiong (University of British Columbia); Mathias Lécuyer (University of British Columbia); Joséphine Gantois (University of British Columbia)	Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023	Zero shot microclimate prediction with deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: While weather station data is a valuable resource for climate prediction, its reliability can be limited in remote locations. Furthermore, making local predictions often relies on sensor data that may not be accessible for a new, unmonitored location. In response to these challenges, we introduce a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations. Authors: Iman Deznabi (UMass); Peeyush Kumar (Microsoft Research); Madalina Fiterau (University of Massachusetts Amherst)	Meta- and Transfer Learning Climate Science & Modeling
NeurIPS 2023	Glacier Movement Prediction with Attention-based Recurrent Neural Networks and Satellite Data (Papers Track) Abstract and authors: (click to expand) Abstract: Studying glacier movements is crucial because of their indications for global climate change and its effects on local land masses. Building on established methods for glacier movement prediction from Landsat-8 satellite imaging data, we develop an attention-based deep learning model for time series data prediction of glacier movements. In our approach, the Normalized Difference Snow Index is calculated from the Landsat-8 spectral reflectance bands for data of the Parvati Glacier (India) to quantify snow and ice in the scene images, which is then used for time series prediction. Based on this data, a newly developed Long-Short Term Memory Encoder-decoder neural network model is trained, incorporating a Multi-head Self Attention mechanism in the decoder. The model shows promising results, making the prediction of optical flow vectors from pure model predictions possible. Authors: Jonas Müller (University of Tübingen); Raphael Braun (University of Tübingen); Hendrik P. A. Lensch (University of Tübingen); Nicole Ludwig (University of Tübingen)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023	A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents (Papers Track) Abstract and authors: (click to expand) Abstract: For low carbon transport planning it's essential to grasp the location-specific cause-and-effect mechanisms that the built environment has on travel. Yet, current research falls short in representing causal relationships between the "6D" urban form variables and travel, generalizing across different regions, and modelling urban form effects at high spatial resolution. Here, we address these gaps by utilizing a causal discovery and an explainable machine learning framework to detect urban form effects on intra-city travel emissions based on high-resolution mobility data of six cities across three continents. We show that distance to center, demographics and density indirectly affect other urban form features and that location-specific influences align across cities, yet vary in magnitude. In addition, the spread of the city and the coverage of jobs across the city are the strongest determinants of travel-related emissions, highlighting the benefits of compact development and associated benefits. Our work is a starting point for location-specific analysis of urban form effects on mobility using causal discovery approaches, which is highly relevant municipalities across continents. Authors: Felix Wagner (TU Berlin, MCC Berlin); Florian Nachtigall (MCC Berlin); Lukas B Franken (University of Edinburgh); Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC)); Marta C. González (Berkeley); Jakob Runge (TU Berlin); Rafael Pereira (IPEA); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))	Cities & Urban Planning Causal & Bayesian Methods
NeurIPS 2023	Unleashing the Autoconversion Rates Forecasting: Evidential Regression from Satellite Data (Papers Track) Abstract and authors: (click to expand) Abstract: High-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM) can be used to understand the interactions between aerosols, clouds, and precipitation processes that currently represent the largest source of uncertainty involved in determining the radiative forcing of climate change. Nevertheless, due to the exceptionally high computing cost required, this simulation-based approach can only be employed for a short period of time within a limited area. Despite the fact that machine learning can mitigate this problem, the related model uncertainties may make it less reliable. To address this, we developed a neural network (NN) model powered with evidential learning to assess the data and model uncertainties applied to satellite observation data. Our study focuses on estimating the rate at which small droplets (cloud droplets) collide and coalesce to become larger droplets (raindrops) – autoconversion rates -- since this is one of the key processes in the precipitation formation of liquid clouds, hence crucial to better understanding cloud responses to anthropogenic aerosols. The results of estimating the autoconversion rates demonstrate that the model performs reasonably well, with the inclusion of both aleatoric and epistemic uncertainty estimation, which improves the credibility of the model and provides useful insights for future improvement. Authors: Maria C Novitasari (University College London); Johannes Quaas (Universität Leipzig); Miguel Rodrigues (University College London)	Climate Science & Modeling Uncertainty Quantification & Robustness
NeurIPS 2023	Detailed Glacier Area Change Analysis in the European Alps with Deep Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Glacier retreat is a key indicator of climate change and requires regular updates of the glacier area. Recently, the release of a new inventory for the European Alps showed that glaciers continued to retreat at about 1.3% per year from 2003 to 2015. The outlines were produced by manually correcting the results of a semi-automatic method applied to Sentinel-2 imagery. In this work we develop a fully-automatic pipeline based on Deep Learning to investigate the evolution of the glaciers in the Alps from 2015 to present (2023). After outlier filtering, we provide individual estimates for around 1300 glaciers, representing 87% of the glacierized area. Regionally we estimate an area loss of -1.8% per year, with large variations between glaciers. Code and data are available at https://github.com/dcodrut/glacier_mapping_alps_tccml. Authors: Codrut-Andrei Diaconu (DLR); Jonathan Bamber (Technical University of Munich)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023	An LSTM-based Downscaling Framework for Australian Precipitation Projections (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding potential changes in future rainfall and their local impacts on Australian communities can inform adaptation decisions worth billions of dollars in insurance, agriculture, and other sectors. This understanding relies on downscaling a large ensemble of coarse Global Climate Models (GCMs), our primary tool for simulating future climate. However, the prohibitively high computational cost of downscaling has been a significant barrier. In response, this study develops a cost-efficient downscaling framework for daily precipitation using Long Short-Term Memory (LSTM) models. The models are trained with ERA5 reanalysis data and a customized quantile loss function to better capture precipitation extremes. The framework is employed to downscale precipitation from a GCM member of the CMIP6 ensemble. We demonstrate the skills of the downscaling models to capture spatial and temporal characteristics of precipitation. We also explore regional future changes in precipitation extremes projected by the downscaled GCM. In general, this framework will enable the generation of a large ensemble of regional future projections for Australian rainfall. This will further enhance the assessment of likely climate risks and the quantification of their uncertainties. Authors: Matthias Bittner (Vienna University of Technology); Sanaa Hobeichi (The University of New South Wales); Muhammad Zawish (Walton Institute, WIT); Samo DIATTA (Assane Seck University of Ziguinchor); Remigius Ozioko (University of Nigeria); Sharon Xu (Indigo Ag); Axel Jantsch (TU Wien)	Extreme Weather Time-series Analysis
NeurIPS 2023	A machine learning pipeline for automated insect monitoring (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology. Authors: Aditya Jain (Mila); Fagner Cunha (Federal University of Amazonas); Michael Bunsen (Mila, eButterfly); Léonard Pasi (EPFL); Anna Viklund (Daresay); Maxim Larrivee (Montreal Insectarium); David Rolnick (McGill University, Mila)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2023	Climate Variable Downscaling with Conditional Normalizing Flows (Papers Track) Abstract and authors: (click to expand) Abstract: Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. This approach allows for a probabilistic interpretation of the predictions, while also capturing the stochasticity inherent in the relationships among fine and coarse spatial scales. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean. Authors: Christina Elisabeth Winkler (Mila); Paula Harder (Mila); David Rolnick (McGill University, Mila)	Climate Science & Modeling Generative Modeling
NeurIPS 2023	Global Coastline Evolution Forecasting from Satellite Imagery using Deep Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Coastal zones are under increasing pressures due to climate change and the increasing population densities in coastal areas around the globe. Our ability to accurately forecast the evolution of the coastal zone is of critical importance to coastal managers in the context of risk assessment and mitigation. Recent advances in artificial intelligence and remote sensing enable the development of automatic large-scale analysis methodologies based on observation data. In this work, we make use of a novel satellite-derived shoreline forecasting dataset and a variant of the common Encoder-Decoder neural network, UNet, in order to predict shoreline change based on spatio-temporal data. We analyze the importance of including the spatial context at the prediction step and we find that it greatly enhances model performance. Overall, the model presented here demonstrates significant shoreline forecasting skill around the globe, achieving a global correlation of 0.77. Authors: Guillaume RIU (Laboratory of Spatial Geophysics and Oceanography Studies); Mahmoud AL NAJAR (Laboratory of Spatial Geophysics and Oceanography Studies); Gregoire THOUMYRE (Laboratory of Spatial Geophysics and Oceanography Studies); Rafael ALMAR (Laboratory of Spatial Geophysics and Oceanography Studies); Dennis Wilson (ISAE)	Oceans & Marine Systems Computer Vision & Remote Sensing
NeurIPS 2023	Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models (Papers Track) Abstract and authors: (click to expand) Abstract: Governments around the world aspire to ground decision-making on evidence. Many of the foundations of policy making — e.g. sensing patterns that relate to societal needs, developing evidence-based programs, forecasting potential outcomes of policy changes, and monitoring effectiveness of policy programs — have the potential to benefit from the use of large-scale datasets or simulations together with intelligent algorithms. These could, if designed and deployed in a way that is well grounded on scientific evidence, enable a more comprehensive, faster, and rigorous approach to policy making. Integrated Assessment Models (IAM) is a broad umbrella covering scientific models that attempt to link main features of society and economy with the biosphere into one modelling framework. At present, these systems are probed by by policy makers and advisory groups in a hypothesis-driven manner. In this paper, we empirically demonstrate that modern Reinforcement Learning can be used to probe IAMs and explore the space of solutions in a more principled manner. While the implication of our results are modest since the environment is simplistic, we believe that this is a stepping stone towards more ambitious use cases, which could allow for effective exploration of policies and understanding of their consequences and limitations. Authors: Theodore LM Wolf (Carbon Re); Nantas Nardelli (CarbonRe); John Shawe-Taylor (University College London); Maria Perez-Ortiz (University College London)	Public Policy Societal Adaptation & Resilience
NeurIPS 2023	IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision (Papers Track) Abstract and authors: (click to expand) Abstract: Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets. This work will enable novel research to improve ice cloud process understanding and hence, reduce uncertainties in a changing climate and help assess geoengineering methods for cirrus clouds. Authors: Kai Jeggle (ETH Zurich); Mikolaj Czerkawski (ESA); Federico Serva (European Space Agency, Italian Space Agency); Bertrand Le Saux (European Space Agency (ESA)); David Neubauer (ETH Zurich); Ulrike Lohmann (ETH Zurich)	Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2023	SAM-CD: Change Detection in Remote Sensing Using Segment Anything Model (Papers Track) Abstract and authors: (click to expand) Abstract: In remote sensing, Change Detection (CD) refers to locating surface changes in the same area over time. Changes can occur due to man-made or natural activities, and CD is important for analyzing climate changes. The recent advancements in satellite imagery and deep learning allow the development of affordable and powerful CD solutions. The breakthroughs in computer vision Foundation Models (FMs) bring new opportunities for better and more flexible remote sensing solutions. However, solving CD using FMs has not been explored before and this work presents the first FM-based deep learning model, SAM-CD. We propose a novel model that adapts the Segment Anything Model (SAM) for solving CD. The experimental results show that the proposed approach achieves the state of the art when evaluated on two challenging benchmark public datasets LEVIR-CD and DSIFN-CD. Authors: Faroq ALTam (Elm Company); Thariq Khalid (Elm Company); Athul Mathew (Elm Company); Andrew Carnell (Elm Company); Riad Souissi (Elm Company)	Computer Vision & Remote Sensing Cities & Urban Planning
NeurIPS 2023	Segment-then-Classify: Few-shot instance segmentation for environmental remote sensing (Papers Track) Abstract and authors: (click to expand) Abstract: Instance segmentation is pivotal for environmental sciences and climate change research, facilitating important tasks from land cover classification to glacier monitoring. This paper addresses the prevailing challenges associated with data scarcity when using traditional models like YOLOv8 by introducing a novel, data-efficient workflow for instance segmentation. The proposed Segment-then-Classify (STC) strategy leverages the zero-shot capabilities of the novel Segment Anything Model (SAM) to segment all objects in an image and then uses a simple classifier such as the Vision Transformer (ViT) to identify objects of interest thereafter. Evaluated on the VHR-10 dataset, our approach demonstrated convergence with merely 40 examples per class. YOLOv8 requires 3 times as much data to achieve the STC's peak performance. The highest performing class in the VHR-10 dataset achieved a near-perfect mAP@0.5 of 0.99 using the STC strategy. However, performance varied greatly across other classes due to the SAM model’s occasional inability to recognize all relevant objects, indicating a need for refining the zero-shot segmentation step. The STC workflow therefore holds promise for advancing few-shot learning for instance segmentation in environmental science. Authors: Yang Hu (University of California, Santa Barbara); Kelly Caylor (UCSB); Anna S Boser (UCSB)	Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023	Surrogate modeling based History Matching for an Earth system model of intermediate complexity (Papers Track) Abstract and authors: (click to expand) Abstract: Climate General Circulation Models (GCMs) constitute the primary tools for climate projections that inform IPCC Assessment Reports. Calibrating, or tuning the parameters of the models can significantly improve their predictions, thus their scientific and societal impacts. Unfortunately, traditional tuning techniques remain time-consuming and computationally costly, even at coarse resolution. A specific challenge for the tuning of climate models lies in the tuning of both fast and slow climatic features: while atmospheric processes adjust on hourly to weekly timescales, vegetation or ocean dynamics drive mechanisms of variability at decadal to millenial timescales. In this work, we explore whether and how History Matching, which uses machine learning based emulators to accelerate and automate the tuning process, is relevant for tuning climate models with multiple timescales. To facilitate this exploration, we work with a climate model of intermediate complexity, yet test experimental tuning protocols that can be directly applied to more complex GCMs to reduce uncertainty in climate projections. Authors: Maya Janvier (Centrale Supélec); Redouane Lguensat (IPSL); Julie Deshayes (LOCEAN IPSL); Aurélien Quiquet (LSCE); Didier Roche (LSCE); V. Balaji (Schmidt Futures)	Climate Science & Modeling
NeurIPS 2023	FireSight: Short-Term Fire Hazard Prediction Based on Active Fire Remote Sensing Data (Papers Track) Abstract and authors: (click to expand) Abstract: Wildfires are becoming unpredictable natural hazards in many regions due to climate change. However, existing state-of-the-art wildfire forecasting tools, such as the Fire Weather Index (FWI), rely solely on meteorological input parameters and have limited ability to model the increasingly dynamic nature of wildfires. In response to the escalating threat, our work addresses this shortcoming in short-term fire hazard prediction. First, we present a comprehensive and high fidelity remotely sensed active fire dataset fused from over 20 satellites. Second, we develop region-specific ML-based 3-7 day wildfire hazard prediction models for hazard South America, Australia, and Southern Europe. The different models cover pixel-wise, spatial and spatio-temporal architectures, and utilize weather, fuel and location data. We evaluate the models using time-based cross-validation and can show superior performance with a PR-AUC score up to 44 times higher compared to the baseline FWI model. Using explainable AI methods, we show that these data-driven models are also capable of learning meaningful physical patterns and inferring region-specific wildfire drivers. Authors: Julia Gottfriedsen (OroraTech GmbH); Johanna Strebl (OroraTech GmbH); Max Berrendorf (Ludwig-Maximilians-Universität München); Martin Langer (OroraTech GmbH); Volker Tresp (Ludwig-Maximilians-Universität München)	Disaster Management and Relief Extreme Weather
NeurIPS 2023	Scaling Sodium-ion Battery Development with NLP (Papers Track) Abstract and authors: (click to expand) Abstract: Sodium-ion batteries (SIBs) have been gaining attention for applications like grid-scale energy storage, largely owing to the abundance of sodium and an expected favorable $/kWh figure. SIBs can leverage the well-established manufacturing knowledge of Lithium-ion Batteries (LIBs), but several materials synthesis and performance challenges for electrode materials need to be addressed. This work extracts a large database of challenges restricting the performance and synthesis of SIB cathode active materials (CAMs) and pairs them with corresponding mitigation strategies from the SIB literature by employing custom natural language processing (NLP) tools. The derived insights enable scientists in research and industry to navigate a large number of proposed strategies and focus on impactful scalability-informed mitigation strategies to accelerate the transition from lab to commercialization. Authors: Mrigi Munjal (Massachusetts Institute of Technology); Thorben Pein (TU Munich); Vineeth Venugopal (Massachusetts Institute of Technology); Kevin Huang (Massachusetts Institute of Technology); Elsa Olivetti (Massachusetts Institute of Technology)	Chemistry & Materials Power & Energy
NeurIPS 2023	The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning. Especially in light of rapid urban growth, identifying housing locations that minimize travel demand and induced CO2 emissions is crucial to mitigate climate change. While the built environment plays an important role, the precise impact on travel behavior is obfuscated by residential self-selection. To address this issue, we propose a double machine learning approach to obtain unbiased, spatially-explicit estimates of the effect of the built environment on travel-related CO2 emissions for each neighborhood by controlling for residential self-selection. We examine how socio-demographics and travel-related attitudes moderate the effect and how it decomposes across the 5Ds of the built environment. Based on a case study for Berlin and the travel diaries of 32,000 residents, we find that the built environment causes household travel-related CO2 emissions to differ by a factor of almost two between central and suburban neighborhoods in Berlin. To highlight the practical importance for urban climate mitigation, we evaluate current plans for 64,000 new residential units in terms of total induced transport CO2 emissions. Our findings underscore the significance of spatially differentiated compact development to decarbonize the transport sector. Authors: Florian Nachtigall (Technical University of Berlin); Felix Wagner (TU Berlin, MCC Berlin); Peter Berrill (Technical University of Berlin); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))	Cities & Urban Planning Causal & Bayesian Methods
NeurIPS 2023	Lightweight, Pre-trained Transformers for Remote Sensing Timeseries (Papers Track) Abstract and authors: (click to expand) Abstract: Machine learning models for parsing remote sensing data have a wide range of societally relevant applications, but labels used to train these models can be difficult or impossible to acquire. This challenge has spurred research into self-supervised learning for remote sensing data. Current self-supervised learning approaches for remote sensing data draw significant inspiration from techniques applied to natural images. However, remote sensing data has important differences from natural images -- for example, the temporal dimension is critical for many tasks and data is collected from many complementary sensors. We show we can create significantly smaller performant models by designing architectures and self-supervised training techniques specifically for remote sensing data. We introduce the Pretrained Remote Sensing Transformer (Presto), a transformer-based model pre-trained on remote sensing pixel-timeseries data. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale. Authors: Gabriel Tseng (NASA Harvest); Ruben Cartuyvels (KULeuven); Ivan Zvonkov (University of Maryland); Mirali Purohit (Arizona State University (ASU)); David Rolnick (McGill University, Mila); Hannah R Kerner (Arizona State University)	Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2023	Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology (Papers Track) Abstract and authors: (click to expand) Abstract: An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidal treatment). To make our predictions, we explore temporal, visual and multi-modal models that combine satellite sensing and deep learning. We find that all of our neural-based approaches improve over conventional buffelgrass green-up models, and discuss how neural model deployment promises significant resource savings. Authors: Lucas Rosenblatt (NYU); Bin Han (University of Washington); Erin Posthumus (USA NPN); Theresa Crimmins (USA NPN); Bill G Howe (University of Washington)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023	Typhoon Intensity Prediction with Vision Transformer (Papers Track) Abstract and authors: (click to expand) Abstract: Predicting typhoon intensity accurately across space and time is crucial for issuing timely disaster warnings and facilitating emergency response. This has vast potential for minimizing life losses and property damages as well as reducing economic and environmental impacts. Leveraging satellite imagery for scenario analysis is effective but also introduces additional challenges due to the complex relations among clouds and the highly dynamic context. Existing deep learning methods in this domain rely on convolutional neural networks (CNNs), which suffer from limited per-layer receptive fields. This limitation hinders their ability to capture long-range dependencies and global contextual knowledge during inference. In response, we introduce a novel approach, namely "Typhoon Intensity Transformer" (Tint), which leverages self-attention mechanisms with global receptive fields per layer. Tint adopts a sequence-to-sequence feature representation learning perspective. It begins by cutting a given satellite image into a sequence of patches and recursively employs self-attention operations to extract both local and global contextual relations between all patch pairs simultaneously, thereby enhancing per-patch feature representation learning. Extensive experiments on a publicly available typhoon benchmark validate the efficacy of Tint in comparison with both state-of-the-art deep learning and conventional meteorological methods. Our code is available at https://github.com/chen-huanxin/Tint. Authors: Huanxin Chen (South China University of Technology); Pengshuai Yin (South China University of Technology); Huichou Huang (City University of Hong Kong); Qingyao Wu (South China University of Technology); Ruirui Liu (Brunel University London); Xiatian Zhu (University of Surrey)	Computer Vision & Remote Sensing Climate Science & Modeling
NeurIPS 2023	Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK (Papers Track) Abstract and authors: (click to expand) Abstract: In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting. This study delves into this emerging trend, presenting our methodologies and outcomes. We harnessed the UK's local ERA5 850 hPa temperature data and refined the U-STN12 global weather forecasting model, tailoring its predictions to the UK's climate nuances. From the ASOS network, we sourced t2m data, representing ground observations across the UK. We employed the advanced kriging method with a polynomial drift term for consistent spatial resolution. Furthermore, Gaussian noise was superimposed on the ERA5 T850 data, setting the stage for ensuing multi-time step virtual observations. Probing into the assimilation impacts, the ASOS t2m data was integrated with the ERA5 T850 dataset. Our insights reveal that while global forecast models can adapt to specific regions, incorporating atmospheric data in DA significantly bolsters model accuracy. Conversely, the direct assimilation of surface temperature data tends to mitigate this enhancement, tempering the model's predictive prowess. Authors: WENQI WANG (Imperial College London); Jacob Bieker (Open Climate Fix); Rossella Arcucci (Imperial College London); Cesar Quilodran-Casas (Imperial College London)	Earth Observation & Monitoring Uncertainty Quantification & Robustness
NeurIPS 2023	Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate and computationally-viable representations of clouds and turbulence are a long-standing challenge for climate model development. Traditional parameterizations that crudely but efficiently approximate these processes are a leading source of uncertainty in long-term projected warming and precipitation patterns. Machine Learning (ML)-based parameterizations have long been hailed as a promising alternative with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. However, these ML variants are often unpredictably unstable and inaccurate in online testing (i.e. in a downstream hybrid simulation task where they are dynamically coupled to the large-scale climate model). These issues are exacerbated in out-of-distribution climates. Certain design decisions such as ``climate-invariant" feature transformation, input vector expansion, and temporal history incorporation have been shown to improve online performance, but they may be insufficient for the mission-critical task of online out-of-distribution generalization. If feature selection and transformations can inoculate hybrid physics-ML climate models from non-physical out-of-distribution extrapolation in a changing climate, there is far greater potential in extrapolating from observational data. Otherwise, training on multiple simulated climates becomes an inevitable necessity. While our results show generalization benefits from these design decisions, such benefits do not sufficiently preclude the necessity of using multi-climate simulated training data. Authors: Jerry Lin (University of California, Irvine); Mohamed Aziz Bhouri (Columbia University); Tom G Beucler (Columbia University & UCI); Sungduk Yu (University of California, Irvine); Michael Pritchard (UCI)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023	Deploying Reinforcement Learning based Economizer Optimization at Scale (Papers Track) Abstract and authors: (click to expand) Abstract: Building operations account for a significant portion of global emissions, contributing approximately 28\% of global greenhouse gas emissions. With anticipated increase in cooling demand due to rising global temperatures, the optimization of rooftop units (RTUs) in buildings becomes crucial for reducing emissions. We focus on the optimization of the economizer logic within RTUs, which balances the mix of indoor and outdoor air. By effectively utilizing free outside air, economizers can significantly decrease mechanical energy usage, leading to reduced energy costs and emissions. We introduce a reinforcement learning (RL) approach that adaptively controls the economizer based on the unique characteristics of individual facilities. We have trained and deployed our solution in the real-world across a distributed building stock. We address the scaling challenges with our cloud-based RL deployment on 10K+ RTUs across 200+ sites. Authors: Ivan Cui (Amazon); Wei Yih Yap (Amazon); Charles Prosper (Independant); Bharathan Balaji (Amazon); Jake Chen (Amazon)	Buildings Reinforcement Learning
NeurIPS 2023	The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation (Papers Track) Abstract and authors: (click to expand) Abstract: Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By pre-releasing water via these structures in advance of extreme weather events, water levels are sufficiently lowered to prevent floods. In this work, we propose FIDLAR, a Forecast Informed Deep Learning Architecture, achieving flood management in watersheds with hydraulic structures in an optimal manner by balancing out flood mitigation and unnecessary wastage of water via pre-releases. We perform experiments with FIDLAR using data from the South Florida Water Management District, which manages a coastal area that is highly prone to frequent storms and floods. Results show that FIDLAR performs better than the current state-of-the-art with several orders of magnitude speedup and with provably better pre-release schedules. The dramatic speedups make it possible for FIDLAR to be used for real-time flood management. The main contribution of this paper is the effective use of tools for model explainability, allowing us to understand the contribution of the various environmental factors towards its decisions. Authors: Jimeng Shi (Florida International University); Vitalii Stebliankin (FIU); Giri Narasimhan (Florida International University)	Disaster Management and Relief Interpretable ML
NeurIPS 2023	Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models (Papers Track) Abstract and authors: (click to expand) Abstract: Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generate perturbed parameter ensemble data and generate surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity. Authors: Yixuan Sun (Argonne National Laboratory); Elizabeth Cucuzzella (Tufts University); Steven Brus (Argonne National Laboratory); Sri Hari Krishna Narayanan (Argonne National Laboratory); Balu Nadiga (Los Alamos National Lab); Luke Van Roekel (Los Alamos National Laboratory); Jan Hückelheim (Argonne National Laboratory); Sandeep Madireddy (Argonne National Laboratory)	Oceans & Marine Systems
NeurIPS 2023	Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia (Papers Track) Abstract and authors: (click to expand) Abstract: Environmental indicators can play a crucial role in forecasting infectious disease outbreaks, holding promise for community-level interventions. Yet, significant gaps exist in the literature regarding the influence of changes in environmental conditions on disease spread over time and across different regions and climates making it challenging to obtain reliable forecasts. This paper aims to propose an approach to predict malaria incidence over time and space by employing a multi-dimensional long short-term memory model (M-LSTM) to simultaneously analyse environmental indicators such as vegetation, temperature, night-time lights, urban/rural settings, and precipitation. We developed and validated a spatio-temporal data fusion approach to predict district-level malaria incidence rates for the year 2017 using spatio-temporal data from 2000 to 2016 across three South Asian countries: Pakistan, India, and Bangladesh. In terms of predictive performance the proposed M-LSTM model results in lower country-specific error rates compared to existing spatio-temporal deep learning models. The data and code have been made publicly available at the study GitHub repository. Authors: Usman Nazir (Lahore University of Management Sciences); Ahzam Ejaz (Lahore University of Management Sciences); Muhammad Talha Quddoos (Lahore University of Management Sciences); Momin Uppal (Lahore University of Management Sciences); Sara khalid (University of Oxford)	Health Climate Science & Modeling
NeurIPS 2023	Contextual Reinforcement Learning for Offshore Wind Farm Bidding (Papers Track) Abstract and authors: (click to expand) Abstract: We propose a framework for applying reinforcement learning to contextual two-stage stochastic optimization and apply this framework to the problem of energy market bidding of an off-shore wind farm. Reinforcement learning could potentially be used to learn close to optimal solutions for first stage variables of a two-stage stochastic program under different contexts. Under the proposed framework, these solutions would be learned without having to solve the full two-stage stochastic program. We present initial results of training using the DDPG algorithm and present intended future steps to improve performance. Authors: David Cole (University of Wisconsin-Madison); Himanshu Sharma (Pacific Northwest National Laboratory); Wei Wang (Pacific Northwest National Laboratory)	Power & Energy Reinforcement Learning
NeurIPS 2023	CityTFT: Temporal Fusion Transformer for Urban Building Energy Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems against the increasing energy demand at urban and neighborhood levels. However, current UBEM methods are mostly physic-based and time-consuming in multiple climate change scenarios. This work proposes CityTFT, a data-driven UBEM framework, to accurately model the energy demands in urban environments. With the empowerment of the underlying TFT framework and an augmented loss function, CityTFT could predict heating and cooling triggers in unseen climate dynamics with an F1 score of 99.98 \% while RMSE of loads of 13571.3750 Wh. Authors: Ting-Yu Dai (The University of Texas at Austin); Dev Niyogi (The University of Texas at Austin); Zoltan Nagy (The University of Texas at Austin)	Buildings Time-series Analysis
NeurIPS 2023	A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Distributed Converter-based Microgrid Voltage Control (Papers Track) Abstract and authors: (click to expand) Abstract: Renewable energy plays a crucial role in mitigating climate change. With the rising use of distributed energy resources (DERs), microgrids (MGs) have emerged as a solution to accommodate high DER penetration. However, controlling MGs' voltage during islanded operation is challenging due to system's nonlinearity and stochasticity. Although multi-agent reinforcement learning (MARL) methods have been applied to distributed MG voltage control, they suffer from bad scalability and are found difficult to control the MG with a large number of DGs due to the well-known curse of dimensionality. To address this, we propose a scalable network-aware reinforcement learning framework which exploits network structure to truncate the critic's Q-function to achieve scalability. Our experiments show effective control of a MG with up to 84 DGs, surpassing the existing maximum of 40 agents in the existing literature. We also compare our framework with state-of-the-art MARL algorithms to show the superior scalability of our framework. Authors: Han Xu (Tsinghua University); Guannan Qu (Carnegie Mellon University)	Power & Energy Reinforcement Learning
NeurIPS 2023	Reinforcement Learning in agent-based modeling to reduce carbon emissions in transportation (Papers Track) Abstract and authors: (click to expand) Abstract: This paper explores the integration of reinforcement learning (RL) into transportation simulations to explore system interventions to reduce greenhouse gas emissions. The study leverages the Behavior, Energy, Automation, and Mobility (BEAM) transportation simulation framework in conjunction with the Berkeley Integrated System for Transportation Optimization (BISTRO) for scenario development. The main objective is to determine optimal parameters for transportation simulations to increase public transport usage and reduce individual vehicle reliance. Initial experiments were conducted on a simplified transportation scenario, and results indicate that RL can effectively find system interventions that increase public transit usage and decrease transportation emissions. Authors: Yuhao Yuan (UC Berkeley); Felipe Leno da Silva (Lawrence Livermore National Laboratory); Ruben Glatt (Lawrence Livermore National Laboratory)	Transportation Reinforcement Learning
NeurIPS 2023	Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas (Papers Track) Abstract and authors: (click to expand) Abstract: Grid decarbonization for climate change requires dispatchable carbon-free energy like nuclear fusion. The tokamak concept offers a promising path for fusion, but one of the foremost challenges in implementation is the occurrence of energetic plasma disruptions. In this study, we delve into Machine Learning approaches to predict plasma state outcomes. Our contributions are twofold: (1) We present a novel application of Continuous Convolutional Neural Networks for disruption prediction and (2) We examine the advantages and disadvantages of continuous models over discrete models for disruption prediction by comparing our model with the previous, discrete state of the art, and show that continuous models offer significantly better performance (Area Under the Receiver Operating Characteristic Curve = 0.974 v.s. 0.799) with fewer parameters. Authors: William F Arnold (KAIST); Lucas Spangher (MIT PSFC); Cristina Rea (MIT PSFC)	Time-series Analysis Hybrid Physical Models
NeurIPS 2023	Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding the future climate is crucial for informed policy decisions on climate change prevention and mitigation. Earth system models play an important role in predicting future climate, requiring accurate representation of complex sub- processes that span multiple time scales and spatial scales. One such process that links seasonal and interannual climate variability to cyclical biological events is tree phenology in deciduous forests. Phenological dates, such as the start and end of the growing season, are critical for understanding the exchange of carbon and water between the biosphere and the atmosphere. Mechanistic prediction of these dates is challenging. Hybrid modelling, which integrates data-driven approaches into complex models, offers a solution. In this work, as a first step towards this goal, train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models. This highlights the potential of data-driven methods to improve climate predictions. We also analyze which variables and aspects of the time series influence the predicted onset of the season, in order to gain a better understanding of the advantages and limitations of our model. Authors: Christian Reimers (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); David Hafezi Rachti (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); Guohua Liu (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); Alexander Winkler (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry)	Climate Science & Modeling Interpretable ML
NeurIPS 2023	RMM-VAE: a machine learning method for identifying probabilistic weather regimes targeted to a local-scale impact variable (Papers Track) Abstract and authors: (click to expand) Abstract: Identifying large-scale atmospheric patterns that modulate extremes in local-scale variables such as precipitation has the potential to improve long-term climate projections as well as extended-range forecasting skill. This paper proposes a novel probabilistic machine learning method, RMM-VAE, based on a variational autoencoder architecture for identifying weather regimes targeted to a local-scale impact variable. The new method is compared to three existing methods in the task of identifying robust weather regimes that are predictive of precipitation over Morocco while capturing the full phase space of atmospheric dynamics over the Mediterranean. RMM-VAE performs well across these different objectives, outperforming linear methods in reconstructing the full phase space and predicting the target variable, highlighting the potential benefit of applying the method to various climate applications such as downscaling and extended-range forecasting. Authors: Fiona R Spuler (University of Reading); Marlene Kretschmer (Universität Leipzig); Yevgeniya Kovalchuck (University College London); Magdalena Balmaseda (ECMWF); Ted Shepherd (University of Reading)	Generative Modeling Climate Science & Modeling
NeurIPS 2023	Accelerating GHG Emissions Inference: A Lagrangian Particle Dispersion Model Emulator Using Graph Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Inverse modelling systems relying on Lagrangian Particle Dispersion Models (LPDMs) are a popular way to quantify greenhouse gas (GHG) emissions using atmospheric observations, providing independent validation to countries' self-reported emissions. However, the increased volume of satellite measurements cannot be fully leveraged due to computational bottlenecks. Here, we propose a data-driven architecture with Graph Neural Networks that emulates the outputs of LPDMs using only meteorological inputs, and demonstrate it in application with preliminary results for satellite measurements over Brazil. Authors: Elena Fillola (University of Bristol); Raul Santos Rodriguez (University of Bristol); Matt Rigby (University of Bristol)	Climate Science & Modeling
NeurIPS 2023	Gaussian Processes for Monitoring Air-Quality in Kampala (Papers Track) Abstract and authors: (click to expand) Abstract: Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in low and middle-income countries have to rely on a sparse allocation of them. In this paper, we investigate the use of Gaussian Processes for both nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. In particular, we focus on the city of Kampala in Uganda, using data from AirQo's network of sensors. We demonstrate the advantage of removing outliers, compare different kernel functions and additional inputs. We also compare two sparse approximations to allow for the large amounts of temporal data in the dataset. Authors: Clara Stoddart (Imperial College London); Lauren Shrack (Massachusetts Institute of Technology); Usman Abdul-Ganiy (AirQo, Makerere University); Richard Sserunjogi (AirQo, Makerere University); Engineer Bainomugisha (AirQo, Makerere University); Deo Okure (AirQo, Makerere University); Ruth Misener (Imperial College London); Jose Pablo Folch (Imperial College London); Ruby Sedgwick (Imperial College London)	Causal & Bayesian Methods Health
NeurIPS 2023	Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data (Papers Track) Overall Best Paper Abstract and authors: (click to expand) Abstract: Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change. Large scale, high resolution data derived from these sensors can be used to inform intervention and policy decision making, but the timeliness and accuracy of these interventions is limited by use of optical data, which cannot operate at night and is affected by adverse weather conditions. Synthetic Aperture Radar (SAR) offers a robust alternative to optical data, but its associated complexities limit the scope of labelled data generation for traditional deep learning. In this work, we apply a self-supervised pretraining scheme, masked autoencoding, to SAR amplitude data covering 8.7\% of the Earth's land surface area, and tune the pretrained weights on two downstream tasks crucial to monitoring climate change - vegetation cover prediction and land cover classification. We show that the use of this pretraining scheme reduces labelling requirements for the downstream tasks by more than an order of magnitude, and that this pretraining generalises geographically, with the performance gain increasing when tuned downstream on regions outside the pretraining set. Our findings significantly advance climate change mitigation by facilitating the development of task and region-specific SAR models, allowing local communities and organizations to deploy tailored solutions for rapid, accurate monitoring of climate change effects. Authors: Matthew J Allen (University of Cambridge); Francisco Dorr (Independent); Joseph A Gallego (National University Of Colombia); Laura Martínez-Ferrer (University of Valencia); Freddie Kalaitzis (University of Oxford); Raul Ramos-Pollan (Universidad de Antioquia); Anna Jungbluth (European Space Agency)	Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023	Sim2Real for Environmental Neural Processes (Papers Track) Abstract and authors: (click to expand) Abstract: Machine learning (ML)-based weather models have recently undergone rapid improvements.These models are typically trained on gridded reanalysis data from numerical data assimilation systems. However, reanalysis data comes with limitations, such as assumptions about physical laws and low spatiotemporal resolution. The gap between reanalysis and reality has sparked growing interest in training ML models directly on observations such as weather stations. Modelling scattered and sparse environmental observations requires scalable and flexible ML architectures, one of which is the convolutional conditional neural process (ConvCNP). ConvCNPs can learn to condition on both gridded and off-the-grid context data to make uncertainty-aware predictions at target locations. However, the sparsity of real observations presents a challenge for data-hungry deep learning models like the ConvCNP. One potential solution is `Sim2Real': pre-training on reanalysis and fine-tuning on observational data. We analyse Sim2Real with a ConvCNP trained to interpolate surface air temperature over Germany, using varying numbers of weather stations for fine-tuning. On held-out weather stations, Sim2Real training substantially outperforms the same model trained only with reanalysis data or only with station data, showing that reanalysis data can serve as a stepping stone for learning from real observations. Sim2Real could enable more accurate models for climate change monitoring and adaptation. Authors: Jonas Scholz (University of Cambridge)	Meta- and Transfer Learning Climate Science & Modeling
NeurIPS 2023	Methane Plume Detection with U-Net Segmentation on Sentinel-2 Image Data (Papers Track) Abstract and authors: (click to expand) Abstract: Methane emissions have a significant impact on increasing global warming. Satellite-based methane detection methods can help mitigate methane emissions, as they provide a constant and global detection. The Sentinel-2 constellation, in particular, offers frequent and publicly accessible images on a global scale. We propose a deep learning approach to detect methane plumes from Sentinel-2 images. We construct a dataset of 5200 satellite images with identified methane plumes, on which we train a U-Net model. Preliminary results demonstrate that the model is able to correctly identify methane plumes on training data, although generalization to new methane plumes remains challenging. All code, data, and models are made available online. Authors: Berenice du Baret (ISAE-Supaero); Simon Finos (ISAE-Supaero); Hugo Guiglion (ISAE-Supaero); Dennis Wilson (ISAE)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023	Machine Learning Assisted Bayesian Calibration of Model Physics Parameters for Wetland Methane Emissions: A Case Study at a FLUXNET-CH4 Site (Papers Track) Abstract and authors: (click to expand) Abstract: Methane (CH4) possesses a notably higher warming potential than carbon dioxide despite its lower atmospheric concentration, making it integral to global climate dynamics. Wetlands stand out as the predominant natural contributor to global methane emissions. Accurate modeling of methane emissions from wetlands is crucial for understanding and predicting climate change dynamics. However, such modeling efforts are often constrained by the inherent uncertainties in model parameters. Our work leverages machine learning (ML) to calibrate five physical parameters of the Energy Exascale Earth System Model (E3SM) land model (ELM) to improve the model’s accuracy in simulating wetland methane emissions. Unlike traditional deterministic calibration methods that target a single set of optimal values for each parameter, Bayesian calibration takes a probabilistic approach and enables capturing the inherent uncertainties in complex systems and providing robust parameter distributions for reliable predictions. However, Bayesian calibration requires numerous model runs and makes it computationally expensive. We employed an ML algorithm, Gaussian process regression (GPR), to emulate the ELM’s methane model, which dramatically reduced the computational time from 6 CPU hours to just 0.72 milliseconds per simulation. We exemplified the procedure at a representative FLUXNET-CH4 site (US-PFa) with the longest continuous methane emission data. Results showed that the default values for two of the five parameters examined were not aligned well with their respective posterior distributions, suggesting that the model’s default parameter values might not always be optimal for all sites, and that site-specific analysis is warranted. In particular, analyses at sites with different vegetation types and wetland characteristics could reveal more useful insights for understanding methane emissions modeling. Authors: Sandeep Chinta (Massachusetts Institute of Technology); Xiang Gao (Massachusetts Institute of Technology); Qing Zhu (Lawrence Berkeley National Laboratory)	Climate Science & Modeling
NeurIPS 2023	ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data (Papers Track) Abstract and authors: (click to expand) Abstract: High-resolution simulations, such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM), provide valuable insights into the complex interactions among aerosols, clouds, and precipitation, which are the major contributors to climate change uncertainty. However, due to their exorbitant computational costs, they can only be employed for a limited period and geographical area. To address this, we propose a more cost-effective method powered by an emerging machine learning approach to better understand the intricate dynamics of the climate system. Our approach involves active learning techniques by leveraging high-resolution climate simulation as an oracle that is queried based on an abundant amount of unlabeled data drawn from satellite observations. In particular, we aim to predict autoconversion rates, a crucial step in precipitation formation, while significantly reducing the need for a large number of labeled instances. In this study, we present novel methods: custom query strategy fusion for labeling instances -- weight fusion (WiFi) and merge fusion (MeFi) -- along with active feature selection based on SHAP. These methods are designed to tackle real-world challenges -- in this case, climate change, with a specific focus on the prediction of autoconversion rates -- due to their simplicity and practicality in application. Authors: Maria C Novitasari (University College London); Johanness Quaas (Universität Leipzig); Miguel Rodrigues (University College London)	Active Learning Climate Science & Modeling
NeurIPS 2023	Towards Causal Representations of Climate Model Data (Papers Track) Abstract and authors: (click to expand) Abstract: Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learning-based emulators trained on existing simulation data can project additional climate scenarios much faster and are computationally efficient. However, they often lack generalizability and interpretability. This work delves into the potential of causal representation learning, specifically the Causal Discovery with Single-parent Decoding (CDSD) method, which could render climate model emulation efficient and interpretable. We evaluate CDSD on multiple climate datasets, focusing on emissions, temperature, and precipitation. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation. Authors: Julien Boussard (Columbia University); Chandni Nagda (University of Illinois at Urbana-Champaign); Julia Kaltenborn (McGill University); Charlotte Lange (Mila); Yaniv Gurwicz (Intel Labs); Peer Nowack (Grantham Institute, Imperial College London. Department of Physics, Imperial College. Data Science Institute, Imperial College. School of Environmental Sciences, University of East Anglia); David Rolnick (McGill University, Mila)	Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2023	ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements? (Papers Track) Abstract and authors: (click to expand) Abstract: Evaluating the accuracy of outputs generated by Large Language Models (LLMs) is especially important in the climate science and policy domain. We introduce the Expert Confidence in Climate Statements (ClimateX) dataset, a novel, curated, expert-labeled dataset consisting of 8094 climate statements collected from the latest Intergovernmental Panel on Climate Change (IPCC) reports, labeled with their associated confidence levels. Using this dataset, we show that recent LLMs can classify human expert confidence in climate-related statements, especially in a few-shot learning setting, but with limited (up to 47%) accuracy. Overall, models exhibit consistent and significant over-confidence on low and medium confidence statements. We highlight implications of our results for climate communication, LLMs evaluation strategies, and the use of LLMs in information retrieval systems. Authors: Romain Lacombe (Stanford University); Kerrie Wu (Stanford University); Eddie Dilworth (Stanford University)	Natural Language Processing Public Policy
NeurIPS 2023	Improving Flood Insights: Diffusion-based SAR to EO Image Translation (Papers Track) Abstract and authors: (click to expand) Abstract: Driven by the climate crisis, the frequency and intensity of flood events are on the rise. Electro-optical (EO) satellite imagery is commonly used for rapid disaster response. However, its utility in flood situations is limited by cloud cover and during nighttime. An alternative method for flood detection involves using Synthetic Aperture Radar (SAR) data. Despite SAR's advantages over EO in these situations, it has a significant drawback: human analysts often struggle to interpret SAR data. This paper proposes a novel framework, Diffusion-based SAR-to-EO Image Translation (DSE). The DSE framework converts SAR images into EO-like imagery, thereby enhancing their interpretability for human analysis. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework provides enhanced visual information and improves performance in all flood segmentation tests. Authors: Minseok Seo (si-analytics); YoungTack Oh (SI Analytics); Doyi Kim (SI Analytics); Dongmin Kang (SIA); Yeji Choi (SI Analytics)	Disaster Management and Relief Earth Observation & Monitoring
NeurIPS 2023	A Wildfire Vulnerability Index for Businesses Using Machine Learning Approaches (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change and housing growth in ecologically vulnerable areas are increasing the frequency and intensity of wildfire events. These events can have profound impact on communities and the economy, but the financial and operational impacts of wildfire on businesses have not been evaluated extensively yet. This paper presents a Wildfire Vulnerability Index (WVI) that measures the risk of a business failing in the 12 months following a wildfire event. An XGBoost algorithm champion model is compared to two challenger models: 1) a model that extends the champion model by incorporating building and property characteristics and 2) a model that utilizes a neural network approach. Results show that while all models perform well in predicting business failure risk post-disaster event, the model that uses building and property characteristics performs best across all performance metrics. As the natural environment shifts under climate change and more businesses are exposed to the consequences of wildfire, the WVI can help emergency managers allocate disaster aid to businesses at the highest risk of failing and can also provide valuable risk insights for portfolio managers and loan processors. Authors: Andrew Byrnes (Dun and Bradstreet); Lisa Stites (Dun and Bradstreet)	Disaster Management and Relief
NeurIPS 2023	Exploring Causal Relationship between Environment and Drizzle Properties using Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Cloud and precipitation properties are controlled by both local and large-scale forcings. Current weather and climate models represent clouds and precipitation through parameterizations that are based on theoretical relationships between environment, clouds, and precipitation. However, these relationships vary considerably among different weather and cloud conditions, thereby leading to inaccurate simulation of cloud and precipitation properties. In this study, we use observations from a site in the Eastern North Atlantic Ocean (28W, 39.5N) to establish a potential causal relationship between large-scale environment, cloud, and precipitation properties. We estimate the structure of a directed acyclic graph (DAG) with the NOTEARS algorithm (Non-combinatorial Optimization via Trace Exponential and Augmented lagRangian for Structure learning) (Zheng et al., 2018 \cite{Zheng2018DAGsLearning}) with a multi-layer perceptron (MLP) neural network classification architecture. We classify liquid water path (LWP), rain rate, and rain drop diameter in two classes based on lower and upper quantiles to identify the governing mechanisms responsible for the two tails of the distribution. We also invoke Random Forest classification to compare our causal model results with conventional decision tree-based approaches. We hypothesize the dominant role of cloud LWP and net radiative cooling in controlling the cloud and precipitation properties. In this way, this study demonstrates the application of a causal machine learning method to identify which environmental properties potentially control cloud and precipitation development. These results will be extremely valuable to both observational and numerical modeling communities as they could help improve the current parameterizations in the weather and climate models. Authors: Piyush Garg (Argonne National Laboratory); Virendra Ghate (Argonne National Laboratory); Maria Cadeddu (Argonne National Laboratory); Bethany Lusch (Argonne National Lab)	Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2023	Asset Bundling for Wind Power Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: The growing penetration of intermittent, renewable generation in US power grids results in increased operational uncertainty. In that context, accurate forecasts are critical, especially for wind generation, which exhibits large variability and is historically harder to predict. To overcome this challenge, this work proposes a novel Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling, machine learning, and forecast reconciliation techniques to accurately predict wind power at the asset, bundle, and fleet level. Notably, our approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks (fleet-level time series) and proposes new asset-bundling criteria to capture the spatio-temporal dynamics of wind power time series. Extensive numerical experiments are conducted on an industry-size dataset of wind farms, demonstrating the benefits of BPR, which consistently and significantly improves forecast accuracy over the baseline approach, especially at the fleet level. Authors: Hanyu Zhang (Georgia Institute of Technology); Mathieu Tanneau (Georgia Institute of Technology); Chaofan Huang (Georgia Institute of Technology); Roshan Joseph (Georgia Institute of Technology); Shangkun Wang (Georgia Institute of Technology); Pascal Van Hentenryck (Georgia Institute of Technology)	Power & Energy Time-series Analysis
NeurIPS 2023	Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network (Papers Track) Abstract and authors: (click to expand) Abstract: Soil organic carbon (SOC) plays a pivotal role in the global carbon cycle, impacting climate dynamics and necessitating accurate estimation for sustainable land and agricultural management. While traditional methods of SOC estimation face resolution and accuracy challenges, recent advancements harness remote sensing, machine learning, and high-resolution satellite mapping. Graph Neural Networks (GNNs), especially when integrated with positional encoders, offer promise in capturing intricate relationships between soil and climate. Using the LUCAS database, this study compared four GNN operators in the positional encoder framework. Results revealed that the PESAGE and PETransformer models outperformed others in SOC estimation, indicating their potential in capturing the complex interplay between SOC and climate features. Our findings underscore the potential of GNN architectures in advancing SOC prediction, paving the way for future explorations with more advanced GNN models. Authors: Weiying Zhao (Deep Planet); Natalia Efremova (Queen Mary University London)	Carbon Capture & Sequestration Computer Vision & Remote Sensing
NeurIPS 2023	Deep Glacier Image Velocimetry: Mapping glacier velocities from Sentinel-2 imagery with deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: Glacier systems are highly sensitive to climate change and play a pivotal role in global mean sea level rise. As such, it is important to monitor how glacier velocities and ice dynamics evolve under a changing climate. The growing wealth of satellite observations has facilitated the inference of glacier velocities from remote sensing imagery through feature tracking algorithms. At present, these rely on sparse cross-correlation estimates as well as computationally expensive optical flow solutions. Here we present a novel use of deep-learning for estimating annual glacier velocities, utilizing the recurrent optical-flow based architecture, RAFT, on consecutive pairs of optical Sentinel-2 imagery. Our results highlight that deep learning can generate dense per-pixel velocity estimates within an automated framework that utilizes Sentinel-2 images over the French Alps. Authors: James B Tlhomole (Imperial College London); Matthew Piggott (Imperial College London); Graham Hughes (Imperial College London)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023	DeepEn2023: Energy Datasets for Edge Artificial Intelligence (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change poses one of the most significant challenges to humanity. As a result of these climatic shifts, the frequency of weather, climate, and water-related disasters has multiplied fivefold over the past 50 years, resulting in over 2$ million deaths and losses exceeding U.S. $3.64 trillion. Leveraging AI-powered technologies for sustainable development and combating climate change is a promising avenue. Numerous significant publications are dedicated to using AI to improve renewable energy forecasting, enhance waste management, and monitor environmental changes in real-time. However, very few research studies focus on making AI itself environmentally sustainable. This oversight regarding the sustainability of AI within the field might be attributed to a mindset gap and the absence of comprehensive energy datasets. In addition, with the ubiquity of edge AI systems and applications, especially on-device learning, there is a pressing need to measure, analyze, and optimize their environmental sustainability, such as energy efficiency. To this end, in this paper, we propose large-scale energy datasets for edge AI, named DeepEn2023, covering a wide range of kernels, state-of-the-art deep neural network models, and popular edge AI applications. We anticipate that DeepEn2023 will enhance transparency regarding sustainability in on-device deep learning across a range of edge AI systems and applications. Authors: XIAOLONG TU (Georgia State University); Anik Mallik (University of North Carolina at Charlotte); Jiang Xie (University of North Carolina at Charlotte); Haoxin Wang (Georgia State University)
NeurIPS 2023	Interpretable machine learning approach to understand U.S. prevented planting events and project climate change impacts (Papers Track) Abstract and authors: (click to expand) Abstract: Extreme weather events in 2019 prevented U.S. farmers from planting crop on a record 19.4 million acres, more than double the previous record. Insurance reports show the majority of these were intended to be corn acres and prevented due to excess soil moisture and precipitation. However, we still lack a detailed understanding of how weather and soil conditions lead to prevented planting, as well as how climate change may impact future outcomes. Machine learning provides a promising approach to this challenge. Here, we model the drivers of prevented corn planting using soil characteristics, monthly weather conditions, and geospatial information. Due to the extreme nature of events causing prevented planting, we use a novel-design zero-inflated regression (ZIR) model that predicts the occurrence of prevented planting as well as the potential severity. We identify key environmental drivers of prevented planting, including May rainfall and soil drainage class. Under climate change scenarios, the model interestingly projects future instances of prevented planting to be less frequent but more severe relative to the historical period. Authors: Haynes Stephens (University of Chicago)	Agriculture & Food
NeurIPS 2023	GraphTransformers for Geospatial Forecasting of Hurricane Trajectories (Papers Track) Abstract and authors: (click to expand) Abstract: In this paper we introduce a novel framework for trajectory prediction of geospatial sequences using GraphTransformers. When viewed across several sequences, we observed that a graph structure automatically emerges between different geospatial points that is often not taken into account for such sequence modeling tasks. We show that by leveraging this graph structure explicitly, geospatial trajectory prediction can be significantly improved. Our GraphTransformer approach improves upon state-of-the-art Transformer based baseline significantly on HURDAT, a dataset where we are interested in predicting the trajectory of a hurricane on a 6 hourly basis. This helps inform evacuation efforts by narrowing down target location by 10 to 20 kilometers along both the north-south and east-west directions. Authors: Satyaki Chakraborty (Carnegie Mellon University); Pallavi Banerjee (University of Washington)	Disaster Management and Relief Climate Science & Modeling
NeurIPS 2023	Integrating Building Survey Data with Geospatial Data: A Cluster-Based Ethical Approach (Papers Track) Abstract and authors: (click to expand) Abstract: This research paper delves into the unique energy challenges faced by Alaska, arising from its remote geographical location, severe climatic conditions, and heavy reliance on fossil fuels while emphasizing the shortage of comprehensive building energy data. The study introduces an ethical framework that leverages machine learning and geospatial techniques to enable the large-scale integration of data, facilitating the mapping of energy consumption data at the individual building level. Utilizing the Alaska Retrofit Information System (ARIS) and the USA Structures datasets, this framework not only identifies and acknowledges limitations inherent in existing datasets but also establishes a robust ethical foundation for data integration. This framework innovation sets a noteworthy precedent for the responsible utilization of data in the domain of climate justice research, ultimately informing the development of sustainable energy policies through an enhanced understanding of building data and advancing ongoing research agendas. Future research directions involve the incorporation of recently released datasets, which provide precise building location data, thereby further validating the proposed ethical framework and advancing efforts in addressing Alaska's intricate energy challenges. Authors: Vidisha Chowdhury (University of Pennsylvania); Gabriela Gongora-Svartzman (Carnegie Mellon University); Erin D Trochim (University of Alaska Fairbanks); Philippe Schicker (Carnegie Mellon University)	Climate Justice Interpretable ML
NeurIPS 2023	Breeding Programs Optimization with Reinforcement Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption. However, breeding programs are challenging due to long turnover times, high-dimensional decision spaces, long-term objectives, and the need to adapt to rapid climate change. This paper introduces the use of Reinforcement Learning (RL) to optimize simulated crop breeding programs. RL agents are trained to make optimal crop selection and cross-breeding decisions based on genetic information. To benchmark RL-based breeding algorithms, we introduce a suite of Gym environments. The study demonstrates the superiority of RL techniques over standard practices in terms of genetic gain when simulated in silico using real-world genomic maize data. Authors: Omar G. Younis (ETH Zurich); Luca Corinzia (ETH Zurich - Information Science & Engineering Group); Ioannis N Athanasiadis (Wageningen University and Research); Andreas Krause (ETH Zürich); Joachim Buhmann (ETH Zurich); Matteo Turchetta (ETH Zurich)	Reinforcement Learning Agriculture & Food
NeurIPS 2023	Discovering Effective Policies for Land-Use Planning (Papers Track) Best Pathway to Impact Abstract and authors: (click to expand) Abstract: How areas of land are allocated for different uses, such as forests, urban, and agriculture, has a large effect on carbon balance, and therefore climate change. Based on available historical data on changes in land use and a simulation of carbon emissions/absorption, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset and the BLUE simulator. It generates Pareto fronts that trade off carbon impact and amount of change customized to different locations, thus providing a potentially useful tool for land-use planning. Authors: Risto Miikkulainen (UT Austin; Cognizant Technology Solutions); Olivier Francon (Cognizant AI Labs); Daniel Young (Cognizant AI Labs); Babak Hodjat (Cognizant AI Labs); Hugo Cunha (Cognizant AI Labs); Jacob Bieker (Open Climate Fix)	Forests
NeurIPS 2023	Probabilistic land cover modeling via deep autoregressive models (Papers Track) Abstract and authors: (click to expand) Abstract: Land use and land cover (LULC) modeling is a challenging task due to long-range dependencies between geographic features and distinct spatial patterns related in topography, ecology, and human development. We explore the usage of a modified Pixel Constrained CNN as applied to inpainting for categorical image data from the National Land Cover Database for producing a diverse set of land use counterfactual scenarios. We find that this approach is effective for producing a distribution of realistic image completions in certain masking configurations. However, the resulting distribution is not well-calibrated in terms of spatial summary statistics commonly used with LULC data and exhibits substantial underdispersion. Authors: Christopher Krapu (Duke University); Ryan Calder (Virginia Tech); Mark Borsuk (Duke University)	Forests Uncertainty Quantification & Robustness
NeurIPS 2023	Simulating the Air Quality Impact of Prescribed Fires Using a Graph Neural Network-Based PM2.5 Emissions Forecasting System (Papers Track) Abstract and authors: (click to expand) Abstract: The increasing size and severity of wildfires across western North America have generated dangerous levels of PM2.5 pollution in recent years. In a warming climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from these prescribed fires, a critical ingredient in determining the fires’ location and time, at hourly to daily time scales remains a challenging problem. This paper proposes a novel integration of prescribed fire simulation with a spatio-temporal graph neural network-based PM2.5 forecasting model. The experiments in this work focus on determining the optimal time for implementing prescribed fires in California as well as quantifying the potential air quality trade-offs involved in conducting more prescribed fires outside the fire season. Authors: Kyleen Liao (Saratoga High School); Jatan Buch (Columbia University); Kara D. Lamb (Columbia University); Pierre Gentine (Columbia University)	Earth Observation & Monitoring Generative Modeling
NeurIPS 2023	Hyperspectral shadow removal with iterative logistic regression and latent Parametric Linear Combination of Gaussians (Papers Track) Abstract and authors: (click to expand) Abstract: Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression(ILR) to learn a spectral basis in which shadows can be linearly classified. We then model the joint distribution of the mean radiance and the projection coefficients of the spectra onto the above basis as a parametric linear combination of Gaussians. We can then extract the maximum likelihood mixing parameter of the Gaussians to estimate the shadow coverage and to correct the shadowed spectra. Our correction scheme reduces correction artefacts at shadow borders. The shadow detection and removal method is applied to hyperspectral images from MethaneAIR, a precursor to the satellite MethaneSAT. Authors: Core Francisco Park (Harvard University); Maya Nasr (Harvard University); Manuel Pérez-Carrasco (University of Concepcion); Eleanor Walker (Harvard University); Douglas Finkbeiner (Harvard University); Cecilia Garraffo (AstroAI at the Center for Astrophysics, Harvard & Smitnsonian)	Earth Observation & Monitoring Uncertainty Quantification & Robustness
NeurIPS 2023	Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale? (Papers Track) Abstract and authors: (click to expand) Abstract: Cooperative Logistics studies the setting where logistics companies pool their resources together to improve their individual performance. Prior literature suggests carbon savings of approximately 22%. If attained globally, this equates to 480,000,000 tonnes of CO2-eq. Whilst well-studied in operations research – industrial adoption remains limited due to a lack of trustworthy cooperation. A key remaining challenge is fair and scalable gain sharing (i.e., how much should each company be fairly paid?). We propose the use of deep reinforcement learning with a neural reward model for coalition structure generation and present early findings. Authors: Stephen Mak (University of Cambridge); Tim Pearce (Microsoft Research); Matthew Macfarlane (University of Amsterdam); Liming Xu (University of Cambridge); Michael Ostroumov (Value Chain Lab); Alexandra Brintrup (University of Cambridge)	Transportation Reinforcement Learning
NeurIPS 2023	Resource Efficient and Generalizable Representation Learning of High-Dimensional Weather and Climate Data (Papers Track) Abstract and authors: (click to expand) Abstract: We study self-supervised representation learning on high-dimensional data under resource constraints. Our work is motivated by applications of vision transformers to weather and climate data. Such data frequently comes in the form of tensors that are both higher dimensional and of larger size than the RGB imagery one encounters in many computer vision experiments. This raises scaling issues and brings up the need to leverage available compute resources efficiently. Motivated by results on masked autoencoders, we show that it is possible to use sampling of subtensors as the sole augmentation strategy for contrastive learning with a sampling ratio of $\sim$1\%. This is to be compared to typical masking ratios of $75\%$ or $90\%$ for image and video data respectively. In an ablation study, we explore extreme sampling ratios and find comparable skill for ratios as low as $\sim$0.0625\%. Pursuing efficiencies, we are finally investigating whether it is possible to generate robust embeddings on dimension values which were not present at training time. We answer this question to the positive by using learnable position encoders which have continuous dependence on dimension values. Authors: Juan Nathaniel (Columbia University); Marcus Freitag (IBM); Patrick Curran (Environment and Climate Change Canada); Isabel Ruddick (Environment and Climate Change Canada); Johannes Schmude (IBM)	Unsupervised & Semi-Supervised Learning Meta- and Transfer Learning
NeurIPS 2023	Difference Learning for Air Quality Forecasting Transport Emulation (Papers Track) Abstract and authors: (click to expand) Abstract: Human health is negatively impacted by poor air quality including increased risk for respiratory and cardiovascular disease. Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events. The National Oceanic and Atmospheric Administration provides air quality forecasting guidance for the Continental United States. Their air quality forecasting model is based on a 15 km spatial resolution; however, the goal is to reach a three km spatial resolution. This is currently not feasible due in part to prohibitive computational requirements for modeling the transport of chemical species. In this work, we describe a deep learning transport emulator that is able to reduce computations while maintaining skill comparable with the existing numerical model. We show how this method maintains skill in the presence of extreme air quality events, making it a potential candidate for operational use. We also explore evaluating how well this model maintains the physical properties of the modeled transport for a given set of species. Authors: Reed R Chen (Johns Hopkins University Applied Physics Laboratory); Christopher Ribaudo (Johns Hopkins University Applied Physics Laboratory); Jennifer Sleeman (University of Maryland, Baltimore County and Johns Hopkins University Applied Physics Laboratory); Chace Ashcraft (JHU/APL); Marisa Hughes (JHU)	Extreme Weather
NeurIPS 2023	Fusion of Physics-Based Wildfire Spread Models with Satellite Data using Generative Algorithms (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change has driven increases in wildfire prevalence, prompting development of wildfire spread models. Advancements in the use of satellites to detect fire locations provides opportunity to enhance fire spread forecasts from numerical models via data assimilation. In this work, a method is developed to infer the history of a wildfire from satellite measurements using a conditional Wasserstein Generative Adversarial Network (cWGAN), providing the information necessary to initialize coupled atmosphere-wildfire models in a physics-informed approach based on measurements. The cWGAN, trained with solutions from WRF-SFIRE, produces samples of fire arrival times (fire history) from the conditional distribution of arrival times given satellite measurements, and allows for assessment of prediction uncertainty. The method is tested on four California wildfires and predictions are compared against measured fire perimeters and reported ignition times. An average Sorensen's coefficient of 0.81 for the fire perimeters and an average ignition time error of 32 minutes suggests that the method is highly accurate. Authors: Bryan Shaddy (University of Southern California); Deep Ray (University of Maryland); Angel Farguell (San Jose State University); Valentina Calaza (University of Southern California); Jan Mandel (University of Colorado Denver); James Haley (Cooperative Institute for Research in the Atmosphere); Kyle Hilburn (Cooperative Institute for Research in the Atmosphere); Derek Mallia (University of Utah); Adam Kochanski (San Jose State University); Assad Oberai (University of Southern California)	Generative Modeling Extreme Weather
NeurIPS 2023	A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network (Papers Track) Abstract and authors: (click to expand) Abstract: To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving high-resolution wind fields left the simulations impractical to test different time lengths and model configurations. This study presents a preliminary development of a physics-informed super-resolution (SR) generative adversarial network (GAN) that super-resolves the three-dimensional (3D) low-resolution wind fields by upscaling x9 times. We develop a pixel-wise self-attention (PWA) module that learns 3D weather dynamics via a self-attention computation followed by a 2D convolution. We also employ a loss term that regularizes the self-attention map during pretraining, capturing the vertical convection process from input wind data. The new PWA SR-GAN shows the high-fidelity super-resolved 3D wind data, learns a wind structure at the high-frequency domain, and reduces the computational cost of a high-resolution wind simulation by x 89.7 times. Authors: Takuya Kurihana (University of Chicago); Levente Klein (IBM Research); Kyongmin Yeo (IBM Research); Daniela Szwarcman (IBM Research); Bruce G Elmegreen (IBM Research); Surya Karthik Mukkavilli (IBM Research, Zurich); Johannes Schmude (IBM)	Generative Modeling Climate Science & Modeling
NeurIPS 2023	Proof-of-concept: Using ChatGPT to Translate and Modernize an Earth System Model from Fortran to Python/JAX (Papers Track) Abstract and authors: (click to expand) Abstract: Earth system models (ESMs) are vital for understanding past, present, and future climate, but they suffer from legacy technical infrastructure. ESMs are primarily implemented in Fortran, a language that poses a high barrier of entry for early career scientists and lacks a GPU runtime, which has become essential for continued advancement as GPU power increases and CPU scaling slows. Fortran also lacks differentiability — the capacity to differentiate through numerical code — which enables hybrid models that integrate machine learning methods. Converting an ESM from Fortran to Python/JAX could resolve these issues. This work presents a semi-automated method for translating individual model components from Fortran to Python/JAX using a large language model (GPT-4). By translating the photosynthesis model from the Community Earth System Model (CESM), we demonstrate that the Python/JAX version results in up to 100x faster runtimes using GPU parallelization, and enables parameter estimation via automatic differentiation. The Python code is also easy to read and run and could be used by instructors in the classroom. This work illustrates a path towards the ultimate goal of making climate models fast, inclusive, and differentiable. Authors: Anthony Zhou (Columbia University), Linnia Hawkins (Columbia University), Pierre Gentine (Columbia University)	Climate Science & Modeling Natural Language Processing
NeurIPS 2023	Learning to forecast diagnostic parameters using pre-trained weather embedding (Papers Track) Abstract and authors: (click to expand) Abstract: Data-driven weather prediction (DDWP) models are increasingly becoming popular for weather forecasting. However, while operational weather forecasts predict a wide variety of weather variables, DDWPs currently forecast a specific set of key prognostic variables. Non-prognostic ("diagnostic") variables are sometimes modeled separately as dependent variables of the prognostic variables (c.f. FourCastNet \cite{pathak2022fourcastnet}), or by including the diagnostic variable as a target in the DDWP. However, the cost of training and deploying bespoke models for each diagnostic variable can increase dramatically with more diagnostic variables, and limit the operational use of such models. Likewise, retraining an entire DDWP each time a new diagnostic variable is added is also cost-prohibitive. We present an two-stage approach that allows new diagnostic variables to be added to an end-to-end DDWP model without the expensive retraining. In the first stage, we train an autoencoder that learns to embed prognostic variables into a latent space. In the second stage, the autoencoder is frozen and "downstream" models are trained to predict diagnostic variables using only the latent representations of prognostic variables as input. Our experiments indicate that models trained using the two-stage approach offer accuracy comparable to training bespoke models, while leading to significant reduction in resource utilization during training and inference. This approach allows for new "downstream" models to be developed as needed, without affecting existing models and thus reducing the friction in operationalizing new models. Authors: Peetak Mitra (Excarta); Vivek Ramavajjala (Excarta)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023	PressureML: Modelling Pressure Waves to Generate Large-Scale Water-Usage Insights in Buildings (Papers Track) Abstract and authors: (click to expand) Abstract: Several studies have indicated that delivering insights and feedback on water usage has been effective in curbing water consumption, making it a pivotal component in achieving long-term sustainability objectives. Despite a significant proportion of water consumption originating from large residential and commercial buildings, there is a scarcity of cost-effective and easy-to-integrate solutions that provide water usage insights in such structures. Furthermore, existing methods for disaggregating water usage necessitate training data and rely on frequent data sampling to capture patterns, both of which pose challenges when scaling up and adapting to new environments. In this work, we aim to solve these challenges through a novel end-to-end approach which records data from pressure sensors and uses time-series classification by DNN models to determine room-wise water consumption in a building. This consumption data is then fed to a novel water disaggregation algorithm which can suggest a set of water-usage events, and has a flexible requirement of training data and sampling granularity. We conduct experiments using our approach and demonstrate its potential as a promising avenue for in-depth exploration, offering valuable insights into water usage on a large scale. Authors: Tanmaey Gupta (Microsoft Research India); Anupam Sobti (IIT Delhi); Akshay Nambi (Microsoft Research)	Buildings Meta- and Transfer Learning
NeurIPS 2023	Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction (Papers Track) Abstract and authors: (click to expand) Abstract: Electrifying heavy-duty trucks offers a substantial opportunity to curtail carbon emissions, advancing toward a carbon-neutral future. However, the inherent challenges of limited battery energy and the sheer weight of heavy-duty trucks lead to reduced mileage and prolonged charging durations. Consequently, battery-swapping services emerge as an attractive solution for these trucks. This paper employs a two-fold approach to investigate the potential and enhance the efficacy of such services. Firstly, spatial-temporal demand prediction models are adopted to predict the traffic patterns for the upcoming hours. Subsequently, the prediction guides an optimization module for efficient battery allocation and deployment. Analyzing the heavy-duty truck data on a highway network spanning over 2,500 miles, our model and analysis underscore the value of prediction/machine learning in facilitating future decision-makings. In particular, we find that the initial phase of implementing battery-swapping services favors mobile battery-swapping stations, but as the system matures, fixed-location stations are preferred. Authors: Linyu Liu (Tsinghua University); Zhen Dai (Chongqing Expressway Group Company); Shiji Song (Department of Automation, Tsinghua University); Xiaocheng Li (Imperial College London); Guanting Chen (The University of North Carolina at Chapel Hill)	Transportation
NeurIPS 2023	Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization (Papers Track) Abstract and authors: (click to expand) Abstract: Monitoring organic matter is pivotal for maintaining soil health and can help inform sustainable soil management practices. While sensor-based soil information offers higher-fidelity and reliable insights into organic matter changes, sampling and measuring sensor data is cost-prohibitive. We propose a multi-modal, scalable framework that can estimate organic matter from remote sensing data, a more readily available data source while leveraging sparse soil information for improving generalization. Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter. Simultaneously we leverage inherent structure in the data and train the model to discriminate among domains using contrastive learning. This causal and contrastive constraint minimization ensures improved generalization and adaptation to other domains. We also shed light on the interpretability of the framework by identifying attributes that are important for improving generalization. Identifying these key soil attributes that affect organic matter will aid in efforts to standardize data collection efforts. Authors: Somya Sharma (U. Minnesota); Swati Sharma (Microsoft Research); RAFAEL PADILHA (Microsoft Research); Emre Kiciman (Microsoft Research); Ranveer Chandra (Microsoft Research)	Carbon Capture & Sequestration Causal & Bayesian Methods
NeurIPS 2023	Inference of CO2 flow patterns--a feasibility study (Papers Track) Abstract and authors: (click to expand) Abstract: As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robust monitoring and detection mechanisms for potential underground CO2 leakage, particularly through pre-existing or induced faults in the storage reservoir's seals. While techniques such as history matching and time-lapse seismic monitoring of CO2 storage have been used successfully in tracking the evolution of CO2 plumes in the subsurface, these methods lack principled approaches to characterize uncertainties related to the CO2 plumes' behavior. Inclusion of systematic assessment of uncertainties is essential for risk mitigation for the following reasons: (i) CO2 plume-induced changes are small and seismic data is noisy; (ii) changes between regular and irregular (e.g., caused by leakage) flow patterns are small; and (iii) the reservoir properties that control the flow are strongly heterogeneous and typically only available as distributions. To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments. While the inferences presented are preliminary in the context of an early CO2 leakage detection system, the results do indicate that inferences with conditional normalizing flows can produce high-fidelity estimates for CO2 plumes with or without leakage. We are also confident that the inferred uncertainty is reasonable because it correlates well with the observed errors. This uncertainty stems from noise in the seismic data and from the lack of precise knowledge of the reservoir's fluid flow properties. Authors: Abhinav Prakash Gahlot (Georgia Institute of Technology); Huseyin Tuna Erdinc (Georgia Institute of Technology); Rafael Orozco (Georgia Institute of Technology); Ziyi Yin (Georgia Institute of Technology); Felix Herrmann (Georgia Institute of Technology)	Carbon Capture & Sequestration Generative Modeling
NeurIPS 2023	Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada (Papers Track) Abstract and authors: (click to expand) Abstract: This study presents the application of generative deep learning techniques to evaluate marine fog visibility nowcasting using the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island (SI), northeast of Canada. The measurements were collected using the Vaisala Forward Scatter Sensor model FD70 and Weather Transmitter model WXT50, and Gill R3A ultrasonic anemometer mounted on the Research Vessel Atlantic Condor. To perform nowcasting, the time series of fog visibility (Vis), wind speed, dew point depression, and relative humidity with respect to water were preprocessed to have lagged time step features. Generative nowcasting of Vis time series for lead times of 30 and 60 minutes were performed using conditional generative adversarial networks (cGAN) regression at visibility thresholds of Vis < 1 km and < 10 km. Extreme gradient boosting (XGBoost) was used as a baseline method for comparison against cGAN. At the 30 min lead time, Vis was best predicted with cGAN at Vis < 1 km (RMSE = 0.151 km) and with XGBoost at Vis < 10 km (RMSE = 2.821 km). At the 60 min lead time, Vis was best predicted with XGBoost at Vis < 1 km (RMSE = 0.167 km) and Vis < 10 km (RMSE = 3.508 km), but the cGAN RMSE was similar to XGBoost. Despite nowcasting Vis at 30 min being quite difficult, the ability of the cGAN model to track the variation in Vis at 1 km suggests that there is potential for generative analysis of marine fog visibility using observational meteorological parameters. Authors: Eren Gultepe (Southern Illinois University Edwardsville); Sen Wang (University of Notre Dame); Byron Blomquist (NOAA); Harindra Fernando (University of Notre Dame); Patrick Kreidl (University of North Florida); David Delene (University of North Dakota); Ismail Gultepe (Ontario Tech University)	Climate Science & Modeling Generative Modeling
NeurIPS 2023	Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion (Papers Track) Abstract and authors: (click to expand) Abstract: Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety. In fact, SAGs are estimated to account for 33-50\% of highway driving externalities. However, insufficient attention has been paid to precisely quantifying where, when, and how much these SAGs take place– necessary for downstream decision making, such as intervention design and policy analysis. A key challenge is that the data available to researchers and governments are typically sparse and aggregated to a granularity that obscures SAGs. To overcome such data limitations, this study thus explores the use of traffic reconstruction techniques for SAG identification. In particular, we introduce a kernel-based method for identifying spatio-temporal features in traffic and leverage bootstrapping to quantify the uncertainty of the reconstruction process. Experimental results on California highway data demonstrate the promise of the method for capturing SAGs. This work contributes to a foundation for data-driven decision making to advance sustainability of traffic systems. Authors: Edgar Ramirez Sanchez (MIT); Shreyaa Raghavan (MIT); Cathy Wu ()	Transportation Uncertainty Quantification & Robustness
NeurIPS 2023	AI assisted Search for Atmospheric CO2 Capture (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon capture technologies is an important tool for mitigating climate change. In recent years, polymer membrane separation methods have emerged as a promising technology for separating CO2 and other green house gases from the atmosphere. Designing new polymers for such tasks is quite difficult. In this work we look at machine learning based methods to search for new polymer designs optimized for CO2 separation. An ensemble ML models is trained on a large database of molecules to predict permeabilities of CO2/N2 and CO2/O2 pairs. We then use search based optimization to discover new polymers that surpass existing polymer designs. Simulations are then done to verify the predicted performance of the new designs. Overall result suggests that ML based search can be used to discover new polymers optimized for carbon capture. Authors: Shivashankar Shivashankar (Student)	Carbon Capture & Sequestration Reinforcement Learning
NeurIPS 2023	Towards Recommendations for Value Sensitive Sustainable Consumption (Papers Track) Abstract and authors: (click to expand) Abstract: Excessive consumption can strain natural resources, harm the environment, and widen societal gaps. While adopting a more sustainable lifestyle means making significant changes and potentially compromising personal desires, balancing sustainability with personal values poses a complex challenge. This article delves into designing recommender systems using neural networks and genetic algorithms, aiming to assist consumers in shopping sustainably without disregarding their individual preferences. We approach the search for good recommendations as a problem involving multiple objectives, representing diverse sustainability goals and personal values. While using a synthetic historical dataset based on real-world sources, our evaluations reveal substantial environmental benefits without demanding drastic personal sacrifices, even if consumers accept only a fraction of the recommendations. Authors: Thomas Asikis (University of Zurich)	Recommender Systems Societal Adaptation & Resilience
NeurIPS 2023	Towards autonomous large-scale monitoring the health of urban trees using mobile sensing (Papers Track) Abstract and authors: (click to expand) Abstract: Healthy urban greenery is a fundamental asset to mitigate climate change phenomenons such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. The current visual or instrumented inspection techniques often require a high amount of human labor making frequent assessments infeasible at a city-wide scale. In this work, we present the GreenScan Project, a ground-based sensing system designed to provide health assessment of urban trees at high space-time resolutions, with low costs. The system utilises thermal and multi-spectral imaging sensors, fused using computer vision models to estimate two tree health indexes, namely NDVI and CTD. Preliminary evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates the potential of autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low-costs. Authors: Akshit Gupta (Delft University of Technology); Martine Rutten (Delft University of Technology); RANGA RAO VENKATESHA PRASAD (TUDelft); Remko Uijlenhoet (Delft University of Technology)	Cities & Urban Planning Computer Vision & Remote Sensing
NeurIPS 2023	Towards Global, General-Purpose Pretrained Geographic Location Encoders (Papers Track) Abstract and authors: (click to expand) Abstract: Location information is essential for modeling tasks in climate-related fields ranging from ecology to the Earth system sciences. However, obtaining meaningful location representation is challenging and requires a model to distill semantic location information from available data, such as remote sensing imagery. To address this challenge, we introduce SatCLIP, a global, general-purpose geographic location encoder that provides vector embeddings summarizing the characteristics of a given location for convenient usage in diverse downstream tasks. We show that SatCLIP embeddings, pretrained on multi-spectral Sentinel-2 satellite data, can be used for various predictive out-of-domain tasks, including temperature prediction and animal recognition in imagery, and outperform existing competing approaches. SatCLIP embeddings also prove helpful in overcoming geographic domain shift. This demonstrates the potential of general-purpose location encoders and opens the door to learning meaningful representations of our planet from the vast, varied, and largely untapped modalities of geospatial data. Authors: Konstantin Klemmer (Microsoft Research); ESTHER ROLF (Google Research); Caleb Robinson (Microsoft AI for Good Research Lab); Lester Mackey (Microsoft Research); Marc Rußwurm (École Polytechnique Fédérale de Lausanne)	Unsupervised & Semi-Supervised Learning Computer Vision & Remote Sensing
NeurIPS 2023	Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes (Papers Track) Abstract and authors: (click to expand) Abstract: We introduce a novel Graph Attention Autoencoder (GAE) with spatial regularization to address the challenge of scalable anomaly detection in spatiotemporal rainfall data across India from 1990 to 2015. Our model leverages a Graph Attention Network (GAT) to capture spatial dependencies and temporal dynamics in the data, further enhanced by a spatial regularization term ensuring geographic coherence. We construct two graph datasets employing rainfall, pressure, and temperature attributes from the Indian Meteorological Department and ERA5 Reanalysis on Single Levels, respectively. Our network operates on graph representations of the data, where nodes represent geographic locations, and edges, inferred through event synchronization, denote significant co-occurrences of rainfall events. Through extensive experiments, we demonstrate that our GAE effectively identifies anomalous rainfall patterns across the Indian landscape. Our work paves the way for sophisticated spatiotemporal anomaly detection methodologies in climate science, contributing to better climate change preparedness and response strategies. Authors: Mihir Agarwal (IIT Gandhinagar); Progyan Das (IIT Gandhinagar); Udit Bhatia (IIT Gandhinagar)	Climate Science & Modeling
NeurIPS 2023	Antarctic Bed Topography Super-Resolution via Transfer Learning (Papers Track) Abstract and authors: (click to expand) Abstract: High-fidelity topography models of the bedrock underneath the thick Antarctic ice sheet can improve scientists' understanding of ice flow and its contributions to sea level rise. However, the bed topography of Antarctica is one of the most challenging surfaces on earth to map, requiring aircrafts with ice penetrating radars to survey the vast and remote continent. We propose FROST, Fusion Regression for Optimal Subglacial Topography, a method that leverages readily available surface topography data from satellites as an auxiliary input modality for bed topography super-resolution. FROST uses a non-parametric Gaussian Process model to transfer local, non-stationary covariance patterns from the ice surface to the bedrock. In a controlled topography reconstruction experiment over complex East Antarctic terrain, our proposed method outperforms bicubic interpolation at all five tested magnification factors, reducing RMSE by 67% at x2, and 25% at x6 magnification. This work demonstrates the opportunity for data fusion methods to advance downstream climate modelling and steward climate change adaptation. Authors: Kim Bente (The University of Sydney); Roman Marchant (University of Technology Sydney); Fabio Ramos (NVIDIA, The University of Sydney)	Computer Vision & Remote Sensing Climate Science & Modeling
NeurIPS 2023	Elucidating the Relationship Between Climate Change and Poverty using Graph Neural Networks, Ensemble Models, and Remote Sensing Data (Papers Track) Abstract and authors: (click to expand) Abstract: Climate and poverty are intrinsically related: regions with extreme temperatures, large temperature variability, and recurring extreme weather events tend to be ranked among the poorest and most vulnerable to climate change. Nevertheless, there currently is no established method to directly estimate the impact of specific climate variables on poverty and to identify geographical regions at high risk of being negatively affected by climate change. In this work, we propose a new approach based on Graph Neural Networks (GNNs) to estimate the effect of climate and remote sensing variables on poverty indicators measuring Education, Health, Living Standards, and Income. Furthermore, we use the trained models and perturbation analyses to identify the geographical regions most vulnerable to the potential variations in climate variables. Authors: Parinthapat Pengpun (Bangkok Christian International School); Alessandro Salatiello (University of Tuebingen)	Climate Finance & Economics Earth Observation & Monitoring
NeurIPS 2023	Sustainability AI copilot: Analyze & ideate at scale to enable positive impact (Papers Track) Abstract and authors: (click to expand) Abstract: With the advances in large scale Foundation Models, web scale access to sustainability data, planetary scale satellite data, the opportunity for larger section of the world population to create positive climate impact can be activated by empowering everyone to ideate via AI copilots. The challenge is: How to enable more people to think & take action on climate & Sustainable Development goals?. We develop AI co-pilots to engage broader community for enabling impact at scale by democratizing climate thinking & ideation tools. We demonstrated how ideating with SAI transforms any seed idea into a holistic one, given the relation between climate & social economic aspects. SAI employs Language Models to represent the voice of the often neglected vulnerable people to the brainstorming discussion for inclusive climate action. We demonstrated how SAI can even create another AI that learns geospatial insights and offers advice to prevent humanitarian disasters from climate change. In this work, we conceptualized, designed, implemented & demonstrated Sustainability AI copilot (SAI) & innovated 4 use cases:- SAI enables sustainability enthusiasts to convert early stage budding thoughts into a robust holistic idea by creatively employing a chain of Large Language Models to think with six-thinking hats ideation. SAI can enables non-experts to become geospatial analysts by generating code to analyze planetary scale satellite data. SAI also ideates in multi-modal latent space to explore climate friendly product designs. SAI also enables human right activists to create awareness about inclusion of vulnerable and persons with disability in the climate conversation. SAI even creates AI apps for persons with disability. We demonstrated working prototypes at the project website, https://sites.google.com/view/climate-copilot . Thus, SAI co-pilot empowers everyone to come together to ideate to make progress on climate and related sustainable development goals. Authors: Rajagopal A (Indian Institute of Technology); Nirmala V (Queen Marys); Immanuel Raja (Karunya University); Arun V (NIT)	Earth Observation & Monitoring Generative Modeling
NeurIPS 2023	Causality and Explainability for Trustworthy Integrated Pest Management (Proposals Track) Abstract and authors: (click to expand) Abstract: Pesticides, widely used in agriculture for pest control, contribute to the climate crisis. Integrated pest management (IPM) is preferred as a climate-smart alternative. However, low adoption rates of IPM are observed due to farmers' skepticism about its effectiveness, so we introduce an enhancing data analysis framework for IPM to combat that. Our framework provides i) robust pest population predictions across diverse environments with invariant and causal learning, ii) interpretable pest presence predictions using transparent models, iii) actionable advice through counterfactual explanations for in-season IPM interventions, iv) field-specific treatment effect estimations, and v) causal inference to assess advice effectiveness. Authors: Ilias Tsoumas (National Observatory of Athens); Vasileios Sitokonstantinou (University of Valencia); Georgios Giannarakis (National Observatory of Athens); Evagelia Lampiri (University of Thessaly); Christos Athanassiou (University of Thessaly); Gustau Camps-Valls (Universitat de València); Charalampos Kontoes (National Observatory of Athens); Ioannis N Athanasiadis (Wageningen University and Research)	Causal & Bayesian Methods Agriculture & Food
NeurIPS 2023	Monitoring Sustainable Global Development Along Shared Socioeconomic Pathways (Proposals Track) Abstract and authors: (click to expand) Abstract: Sustainable global development is one of the most prevalent challenges facing the world today, hinging on the equilibrium between socioeconomic growth and environmental sustainability. We propose approaches to monitor and quantify sustainable development along the Shared Socioeconomic Pathways (SSPs), including mathematically derived scoring algorithms, and machine learning methods. These integrate socioeconomic and environmental datasets, to produce an interpretable metric for SSP alignment. An initial study demonstrates promising results, laying the groundwork for the application of different methods to the monitoring of sustainable global development. Authors: Michelle Wan (University of Cambridge); Jeff Clark (University of Bristol); Edward Small (Royal Melbourne Institute of Technology); Elena Fillola (University of Bristol); Raul Santos Rodriguez (University of Bristol)	Public Policy Time-series Analysis
NeurIPS 2023	Understanding Climate Legislation Decisions with Machine Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Effective action is crucial in order to avert climate disaster. Key in enacting change is the swift adoption of climate positive legislation which advocates for climate change mitigation and adaptation. This is because government legislation can result in far-reaching impact, due to the relationships between climate policy, technology, and market forces. To advocate for legislation, current strategies aim to identify potential levers and obstacles, presenting an opportunity for the application of recent advances in machine learning language models. Here we propose a machine learning pipeline to analyse climate legislation, aiming to investigate the feasibility of natural language processing for the classification of climate legislation texts, to predict policy voting outcomes. By providing a model of the decision making process, the proposed pipeline can enhance transparency and aid policy advocates and decision makers in understanding legislative decisions, thereby providing a tool to monitor and understand legislative decisions towards climate positive impact. Authors: Jeff Clark (University of Bristol); Michelle Wan (University of Cambridge); Raul Santos Rodriguez (University of Bristol)	Public Policy Natural Language Processing
NeurIPS 2023	Mapping the Landscape of Artificial Intelligence in Climate Change Research: A Meta-Analysis on Impact and Applications (Proposals Track) Abstract and authors: (click to expand) Abstract: This proposal advocates a comprehensive and systematic analysis aimed at mapping and characterizing the intricate landscape of Artificial Intelligence and Machine Learning applications and their impacts within the domain of climate change research, both in adaption and mitigation efforts. Notably, a significant upswing in this interdisciplinary intersection has been observed since 2020. Utilizing advanced topic clustering techniques and qualitative analysis, we have discerned 12 distinct macro areas that supplement, enrich, and expand upon those identified in prior research. The primary objective of this undertaking is to furnish a data-rich panoramic view and informative insights regarding the functions and tools of the mentioned disciplines. Our intention is to offer valuable guidance to the scholarly community and propel further research endeavors, encouraging meticulous examinations of research trends and gaps in addressing the formidable challenges posed by climate change and the climate crisis. Authors: Christian Burmester (Osnabrück University); Teresa Scantamburlo (UniversityofVenice)	Natural Language Processing Behavioral and Social Science
NeurIPS 2023	Unlocking the Potential of Renewable Energy Through Curtailment Prediction (Proposals Track) Abstract and authors: (click to expand) Abstract: A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able to predict when and where renewable curtailment occurs would improve renewable utilization. The core of this work is to enable the machine learning community to help decarbonize electricity grids by unlocking the potential of renewable energy through curtailment prediction. Authors: Bilge Acun (Meta / FAIR); Brent Morgan (Meta); Henry Richardson (WattTime); Nat Steinsultz (WattTime); Carole-Jean Wu (Meta / FAIR)	Power & Energy
NeurIPS 2023	Assessing data limitations in ML-based LCLU (Proposals Track) Abstract and authors: (click to expand) Abstract: This study addresses the accuracy challenge in Global Land Use and Land Cover (LULC) maps, crucial for policy making towards climate change mitigation. We evaluate two LULC products based on advanced machine learning techniques across two representative nations, Ecuador and Germany, employing a novel accuracy metric. The analysis unveils a notable accuracy enhancement in the convolutional neural network-based approach against the random forest model used for comparison. Our findings emphasize the potential of sophisticated machine learning methodologies in advancing LULC mapping accuracy, an essential stride towards data-driven, climate-relevant land management and policy decisions. Authors: Angel Encalada-Davila (ESPOL); Christian Tutiven (ESPOL University); Jose E Cordova-Garcia (ESPOL)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023	Predicting Adsorption Energies for Catalyst Screening with Transfer Learning Using Crystal Hamiltonian Graph Neural Network (Proposals Track) Abstract and authors: (click to expand) Abstract: As the world moves towards a clean energy future to mitigate the risks of climate change, the discovery of new catalyst materials plays a significant role in enabling the sustainable production and transformation of energy [2]. The development and verification of fast, accurate, and efficient artificial intelligence and machine learning techniques is critical to shortening time-intensive calculations, reducing costs, and improving computational feasibility. We propose applying the Crystal Hamiltonian Graph Neural Network (CHGNet) on the OC20 dataset in order to iteratively perform structure-to-energy and forces calculations and identify the lowest energy across relaxed structures for a given adsorbate-surface combination. CHGNet's predictions will be compared and benchmarked to corresponding values calculated by density functional theory (DFT) [7] and other models to determine its efficacy. Authors: Angelina Chen (Foothill College/Lawrence Berkeley National Lab); Hui Zheng (Lawrence Berkeley National Lab); Paula Harder (Mila)	Meta- and Transfer Learning Chemistry & Materials
NeurIPS 2023	Physics-informed DeepONet for battery state prediction (Proposals Track) Abstract and authors: (click to expand) Abstract: Electrification has emerged as a pivotal trend in the energy transition to address climate change, leading to a substantial surge in the demand for batteries. Accurately predicting the internal states and performance of batteries assumes paramount significance, as it ensures the safe and stable operation of batteries and informs decision-making processes, such as optimizing battery operation for arbitrage opportunities. However, current models struggle to strike a balance between precision and computational efficiency or are limited in their applicability to specific scenarios. We aim to adopt a physics-informed deep operator network (PI-DeepONet) for internal battery state estimation based on the rigorous P2D model, which can simultaneously achieve high precision and computational efficiency. Furthermore, it exhibits promising prospects for extension beyond lithium-ion batteries to encompass various battery technologies. Authors: Keyan Guo (Peking University)	Power & Energy Hybrid Physical Models
NeurIPS 2023	Decarbonizing Maritime Operations: A Data-Driven Revolution (Proposals Track) Abstract and authors: (click to expand) Abstract: The maritime industry faces an unprecedented challenge in the form of decarbonization. With strict emissions reduction targets in place, the industry is turning to machine learning-based decision support models to achieve sustainability goals. This proposal explores the transformative potential of digitalization and machine learning approaches in maritime operations, from optimizing ship speeds to enhancing supply chain management. By examining various machine learning techniques, this work provides a roadmap for reducing emissions while improving operational efficiency in the maritime sector. Authors: Ismail Bourzak (UQAR); Loubna Benabou (UQAR); Sara El Mekkaoui (DNV); Abdelaziz Berrado (EMI Engineering School)	Oceans & Marine Systems
NeurIPS 2023	High-resolution Global Building Emissions Estimation using Satellite Imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: Globally, buildings account for 30% of end-use energy consumption and 27% of energy sector emissions, and yet the building sector is lacking in low-temporal-latency, high-spatial-resolution data on energy consumption and resulting emissions. Existing methods tend to either have low resolution, high latency (often a year or more), or rely on data typically unavailable at scale (such as self-reported energy consumption). We propose a machine learning based bottom-up model that combines satellite-imagery-derived features to compute Scope 1 global emissions estimates both for residential and commercial buildings at a 1 square km resolution with monthly global updates. Authors: Paul J Markakis (Duke University); Jordan Malof (University of Montana); Trey Gowdy (Duke University); Leslie Collins (Duke University); Aaron Davitt (WattTime); Gabriela Volpato (WattTime); Kyle Bradbury (Duke University)	Buildings Computer Vision & Remote Sensing
NeurIPS 2023	Sand Mining Watch: Leveraging Earth Observation Foundation Models to Inform Sustainable Development (Proposals Track) Abstract and authors: (click to expand) Abstract: As the major ingredient of concrete and asphalt, sand is vital to economic growth, and will play a key role in aiding the transition to a low carbon society. However, excessive and unregulated sand mining in the Global South has high socio-economic and environmental costs, and amplifies the effects of climate change. Sand mines are characterized by informality and high temporal variability, and data on the location and extent of these mines tends to be sparse. We propose to build custom sand-mine detection tools by fine-tuning foundation models for earth observation, which leverage self supervised learning - a cost-effective and powerful approach in sparse data regimes. Our preliminary results show that these methods outperform fully supervised approaches, with the best performing model achieving an average precision score of 0.57 for this challenging task. These tools allow for real-time monitoring of sand mining activity and can enable more effective policy and regulation, to inform sustainable development. Authors: Ando Shah (UC Berkeley); Suraj R Nair (UC Berkeley); Tom Boehnel (TU Munich); Joshua Blumenstock (University of California, Berkeley)	Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2023	Understanding Insect Range Shifts with Out-of-Distribution Detection (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate change is inducing significant range shifts in insects and other organisms. Large-scale temporal data on populations and distributions are essential for quantifying the effects of climate change on biodiversity and ecosystem services, providing valuable insights for both conservation and pest management. With images from camera traps, we aim to use Mahalanobis distance-based confidence scores to automatically detect new moth species in a region. We intend to make out-of-distribution detection interpretable by identifying morphological characteristics of different species using Grad-CAM. We hope this algorithm will be a useful tool for entomologists to study range shifts and inform climate change adaptation. Authors: Yuyan Chen (McGill University, Mila); David Rolnick (McGill University, Mila)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2023	Aquaculture Mapping: Detecting and Classifying Aquaculture Ponds using Deep Learning (Tutorials Track) Abstract and authors: (click to expand) Abstract: Mapping aquaculture ponds is critical for restoration, conservation, and climate adaptation efforts. Aquaculture can contribute to high levels of water pollution from untreated effluent and negatively impact coastal ecosystems. Large-scale aquaculture is also a significant driver in mangrove deforestation, thus reducing the world’s carbon sinks and exacerbating the effects of climate change. However, finding and mapping these ponds on the ground can be highly labor and time-intensive. Most existing automated techniques are focused only on spatial location and do not consider production intensification, which is also crucial to understanding their impact on the surrounding ecosystem. We can classify them into two main types: a) Extensive ponds, which are large, irregularly-shaped ponds that rely on natural productivity, and b) intensive ponds which are smaller and regularly shaped. Intensive ponds use machinery such as aerators that maximize production and also result in the characteristic presence of air bubbles on the pond’s surface. The features of these two types of ponds make them distinguishable and detectable from satellite imagery. In this tutorial, we will discuss types of aquaculture ponds in detail and demonstrate how they can be detected and classified using satellite imagery. The tutorial will introduce an open dataset of human-labeled aquaculture ponds in the Philippines and Indonesia. Using this dataset, the tutorial will use semantic segmentation to map out similar ponds over an entire country and classify them as either extensive or intensive, going through the entire process of i) satellite imagery retrieval, ii) preprocessing these images into a training-ready dataset, iii) model training, and iv) finally model rollout on a sample area. Throughout, the tutorial will leverage PyTorch Lightning, a machine learning framework that provides a simplified and streamlined interface for model experimentation and deployment. This tutorial aims to discuss the relevance of aquaculture ponds in climate adaptation and equip users with the necessary inputs and tools to perform their own ML-powered earth observation projects. Authors: John Christian G Nacpil (Thinking Machines Data Science, Inc.); Joshua Cortez (Thinking Machines Data Science)	Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023	Zero-Emission Vehicle Intelligence (ZEVi): Effectively Charging Electric Vehicles at Scale Without Breaking Power Systems (or the Bank) (Tutorials Track) Abstract and authors: (click to expand) Abstract: Transportation contributes to 29% of all greenhouse gas (GHG) emissions in the US, of which 58% are from light-duty vehicles and 28% from medium-to-heavy duty vehicles (MHDVs) [1]. Battery electric vehicles (EVs) emit 90% less life cycle GHGs than their internal combustion engine (ICEV) counterparts [2], but currently only comprise 2% of all vehicles in the U.S. EVs thus represent a crucial step in decarbonizing road transportation. One major challenge in replacing ICEVs with EVs at scale is the ability to charge a large number of EVs within the constraints of power systems in a cost-effective way. This is an especially prominent problem for MHDVs used in commercial fleets such as shuttle buses and delivery trucks, as they generally require more energy to complete assigned trips compared to light-duty vehicles. In this tutorial, we describe the myriad challenges in charging EVs at scale and define common objectives such as minimizing total load on power systems, minimizing fleet operating costs, as well as maximizing vehicle state of charge and onsite photovoltaic energy usage. We discuss common constraints such as vehicle trip energy requirements, charging station power limits, and limits on vehicles’ time to charge between trips. We survey several different methods to formulate EV charging and energy dispatch as a mathematically solvable optimization problem, using tools such as convex optimization, Markov decision process (MDP), and reinforcement learning (RL). We introduce a commercial application of model-based predictive control (MPC) algorithm, ZEVi (Zero Emission Vehicle intelligence), which solves optimal energy dispatch strategies for charging sessions of commercial EV fleets. Using a synthetic dataset modeled after a real fleet of electric school buses, we engage the audience with a hands-on exercise applying ZEVi to find the optimal charging strategy for a commercial fleet. Lastly, we briefly discuss other contexts in which methods originating from process control and deep learning, like MPC and RL, can be applied to solve problems related to climate change mitigation and adaptation. With the examples provided in this tutorial, we hope to inspire the audience to come up with their own creative ways to apply these methods in different fields within the climate domain. References [1] EPA (2023). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2021. U.S. Environmental Protection Agency, EPA 430-R-23-002. [2] Verma, S., Dwivedi, G., & Verma, P. (2022). Life cycle assessment of electric vehicles in comparison to combustion engine vehicles: A review. Materials Today: Proceedings, 49, 217-222. Authors: Shasha Lin (NextEra Mobility); Jonathan Brophy (NextEra Mobility); Tamara Monge (NextEra Mobility); Jamie Hussman (NextEra Mobility); Michelle Lee (NextEra Mobility); Sam Penrose (NextEra Mobility)	Transportation Reinforcement Learning
NeurIPS 2023	Agile Modeling for Bioacoustic Monitoring (Tutorials Track) Abstract and authors: (click to expand) Abstract: Bird, insect, and other wild animal populations are rapidly declining, highlighting the need for better monitoring, understanding, and protection of Earth’s remaining wild places. However, direct monitoring of biodiversity is difficult. Passive Acoustic Monitoring (PAM) enables detection of the vocalizing species in an ecosystem, many of which can be difficult or impossible to detect by satellite or camera trap. Large-scale PAM deployments using low-cost devices allow measuring changes over time and responses to environmental changes, and targeted deployments can discover and monitor endangered or invasive species. Machine learning methods are needed to analyze the thousands or even millions of hours of audio produced by large-scale deployments. But there are a massive number of potential signals to target for bioacoustic measurement, and many of the most interesting lack training data. Many rare species are difficult to observe. Detecting specific call-types and juvenile calls can give further insight into behavior and population health, but almost no structured datasets exist for these use-cases. No single classifier can address all of these needs, so practitioners regularly need to create new classifiers to address novel problems. Soundscape annotation efforts are very expensive, and machine learning experts are scarce, creating a bottleneck on analysis. We aim to eliminate the bottleneck by providing an efficient, self-contained active learning workflow for biologists. In this tutorial, we present an integrated workflow for analyzing large unlabeled bioacoustic datasets, adapting new agile modeling techniques to audio. Our goal is to allow experts to create a new high quality classifier for a novel class with under one hour of effort. We achieve this by leveraging transfer learning from high-quality bioacoustic models, vector search over audio databases, and lightweight Python notebook UX. The workflow can begin from a single example, proceeds through an efficient active learning loop, and finally applies the produced classifier to a large mass of unlabeled data to produce insights for ecologists and land managers. Authors: tom denton (google); Jenny Hamer (Google Research); Rob Laber (Google)	Ecosystems & Biodiversity Active Learning
ICLR 2023	Tutorial: Quantus x Climate - Applying explainable AI evaluation in climate science (Tutorials Track) Abstract and authors: (click to expand) Abstract: Explainable artificial intelligence (XAI) methods shed light on the predictions of deep neural networks (DNNs). In the climate context, XAI has been applied to improve and validate deep learning (DL) methods while providing researchers with new insight into physical processes. However, the evaluation, validation and selection of XAI methods are challenging due to often lacking ground truth explanations. In this tutorial, we introduce the XAI evaluation package Quantus to the climate community. We start by providing the users with pre-processed input and output data alongside a convolutional neural network (CNN) trained to assign yearly temperature maps to classes according to their decade. We explain the network prediction of an example temperature map using five different explanation techniques Gradient GradientShap, IntegratedGradients, LRP-z and Occlusion. By visually analyzing each explanation method around the North Atlantic (NA) cooling patch 10-80W, 20-60N, we provide a motivating example that shows that different explanations may disagree in their explained evidence which subsequently can lead to different scientific interpretation and potentially, misleading conclusions. We continue by introducing Quantus including the explanation properties that can be evaluated such as robustness, faithfulness, complexity, localization and randomization. We guide the participants towards a practical understanding of XAI evaluation by demonstrating how metrics differ in their scoring and interpretation. Moreover, we teach the participants to compare and select an appropriate XAI method by performing a comprehensive XAI evaluation. Lastly, we return to the motivating example, highlighting how Quantus can facilitate well-founded XAI research in climate science. Authors: Philine L Bommer (TU Berlin); Anna Hedström (Technische Universität Berlin); Marlene Kretschmer (University of Reading); Marina M.-C. Höhne (TU Berlin)	Interpretable ML Climate Science & Modeling
ICLR 2023	CityLearn: A Tutorial on Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities (Tutorials Track) Abstract and authors: (click to expand) Abstract: Buildings are responsible for up to 75% of electricity consumption in the United States. Grid-Interactive Efficient Buildings can provide flexibility to solve the issue of power supply-demand mismatch, particularly brought about by renewables. Their high energy efficiency and self-generating capabilities can reduce demand without affecting the building function. Additionally, load shedding and shifting through smart control of storage systems can further flatten the load curve and reduce grid ramping cost in response to rapid decrease in renewable power supply. The model-free nature of reinforcement learning control makes it a promising approach for smart control in grid-interactive efficient buildings, as it can adapt to unique building needs and functions. However, a major challenge for the adoption of reinforcement learning in buildings is the ability to benchmark different control algorithms to accelerate their deployment on live systems. CityLearn is an open source OpenAI Gym environment for the implementation and benchmarking of simple and advanced control algorithms, e.g., rule-based control, model predictive control or deep reinforcement learning control thus, provides solutions to this challenge. This tutorial leverages CityLearn to demonstrate different control strategies in grid-interactive efficient buildings. Participants will learn how to design three controllers of varying complexity for battery management using a real-world residential neighborhood dataset to provide load shifting flexibility. The algorithms will be evaluated using six energy flexibility, environmental and economic key performance indicators, and their benefits and shortcomings will be identified. By the end of the tutorial, participants will acquire enough familiarity with the CityLearn environment for extended use in new datasets or personal projects. Authors: Kingsley E Nweye (The University of Texas at Austin); Allen Wu (The University of Texas at Austin); Hyun Park (The University of Texas at Austin); Yara Almilaify (The University of Texas at Austin); Zoltan Nagy (The University of Texas at Austin)	Buildings Cities & Urban Planning Power & Energy Reinforcement Learning Time-series Analysis
ICLR 2023	Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector (Tutorials Track) Abstract and authors: (click to expand) Abstract: To cope with climate change, the energy system is undergoing a massive transformation. With the electrification of all sectors, the power grid is facing high additional demand. As a result, the digitization of the grid is becoming more of a focus. The smart grid relies heavily on the increasing deployment of smart electricity meters around the world. The corresponding smart meter data is typically a time series of power or energy measurements with a resolution of 1s to 60 min. This data provides valuable insights and opportunities for monitoring and controlling activities in the power grid. In this tutorial, we therefore provide an overview of best practices for analyzing smart meter data. We focus on machine learning applications and low resolution (15-60 minutes) energy data in a residential setting. We only use real-world datasets and cover use-cases that are highly relevant for practical applications. Although this tutorial is specifically tailored to an audience from the energy domain, we believe that anyone from the data analytics and machine learning community can benefit from it, as many techniques are applicable to any time series data. Through our tutorial, we hope to foster new ideas, contribute to an interdisciplinary exchange between different research fields, and educate people about energy use. Authors: Tobias Brudermueller (ETH Zurich); Markus Kreft (ETH Zurich)	Power & Energy Buildings Time-series Analysis
ICLR 2023	Mitigating climate and health impact of small-scale kiln industry using multi-spectral classifier and deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: Industrial air pollution has a direct health impact and is a major contributor to climate change. Small scale industries particularly bull-trench brick kilns are one of the major causes of air pollution in South Asia often creating hazardous levels of smog that is injurious to human health. To mitigate the climate and health impact of the kiln industry, fine-grained kiln localization at different geographic locations is needed. Kiln localization using multi-spectral remote sensing data such as vegetation index results in a noisy estimates whereas use of high-resolution imagery is infeasible due to cost and compute complexities. This paper proposes a fusion of spatio-temporal multi-spectral data with high-resolution imagery for detection of brick kilns within the "Brick-Kiln-Belt" of South Asia. We first perform classification using low-resolution spatio-temporal multi-spectral data from Sentinel-2 imagery by combining vegetation, burn, build up and moisture indices. Then orientation aware object detector: YOLOv3 (with theta value) is implemented for removal of false detections and fine-grained localization. Our proposed technique, when compared with other benchmarks, results in a 21 times improvement in speed with comparable or higher accuracy when tested over multiple countries. Authors: Usman Nazir (Lahore University of Management Sciences); Murtaza Taj (Lahore University of Management Sciences); Momin Uppal (Lahore University of Management Sciences); Sara khalid (University of Oxford)	Earth Observation & Monitoring Health
ICLR 2023	Global Flood Prediction: a Multimodal Machine Learning Approach (Papers Track) Abstract and authors: (click to expand) Abstract: Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel mul- timodal machine learning approach for multi-year global flood risk prediction, combining geographical information and historical natural disaster dataset. Our multimodal framework employs state-of-the-art processing techniques to extract embeddings from each data modality, including text-based geographical data and tabular-based time-series data. Experiments demonstrate that a multimodal ap- proach, that is combining text and statistical data, outperforms a single-modality approach. Our most advanced architecture, employing embeddings extracted us- ing transfer learning upon DistilBert model, achieves 75%-77% ROCAUC score in predicting the next 1-5 year flooding event in historically flooded locations. This work demonstrates the potentials of using machine learning for long-term planning in natural disaster management Authors: Cynthia Zeng (MIT); Dimitris Bertsimas (MIT)	Extreme Weather Disaster Management and Relief
ICLR 2023	Improving global high-resolution Earth system model simulations of precipitation with generative adversarial networks (Papers Track) Abstract and authors: (click to expand) Abstract: Precipitation extremes are expected to become stronger and more frequent in response to anthropogenic global warming. Accurately projecting the ecological and socioeconomic impacts is an urgent task. Impact models are developed and calibrated with observation-based data but rely on Earth system model (ESM) output for future scenarios. ESMs, however, exhibit significant biases in their output because they cannot fully resolve complex cross-scale interactions of processes that produce precipitation cannot. State-of-the-art bias correction methods only address errors in the simulated frequency distributions, locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here we show that a post-processing method based on physically constrained generative adversarial networks (GANs) can correct biases of a state-of-the-art global ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions similarly well to a gold-standard ESM bias-adjustment framework, it strongly outperforms existing methods in correcting spatial patterns. Our study highlights the importance of physical constraints in neural networks for out-of-sample predictions in the context of climate change. Authors: Philipp Hess (Technical University of Munich)	Generative Modeling Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Hybrid Physical Models
ICLR 2023	Machine Learning for Advanced Building Construction (Papers Track) Abstract and authors: (click to expand) Abstract: High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system. Authors: Hilary Egan (NREL); Clement Fouquet (Trimble Inc.); Chioke Harris (NREL)	Buildings
ICLR 2023	Coregistration of Satellite Image Time Series Through Alignment of Road Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Due to climate change, thawing permafrost affects transportation infrastructure in northern regions. Tracking deformations over time of these structures can allow identifying the most vulnerable sections to permafrost degradation and implement climate adaptation strategies. The Sentinel-2 mission provides data well-suited for multitemporal analysis due to its high temporal resolution and multispectral coverage. However, the geometrical misalignment of Sentinel-2 imagery makes this analysis challenging. Towards the goal of estimating the deformation of linear infrastructure in northern Canada, we propose an automatic subpixel coregistration algorithm for satellite image time series based on the matching of binary masks of roads produced by a deep learning model. We demonstrate the feasibility of achieving subpixel coregistration through alignment of roads on a small dataset of high-resolution Sentinel-2 images from the region of Gillam in northern Canada. This is the first step towards training a road deformation prediction model. Authors: Andres Felipe Perez Murcia (University of Manitoba); Pooneh Maghoul (University of Manitoba); Ahmed Ashraf (University of Manitoba)	Earth Observation & Monitoring Transportation Computer Vision & Remote Sensing
ICLR 2023	Estimating Residential Solar Potential using Aerial Data (Papers Track) Abstract and authors: (click to expand) Abstract: Project Sunroof estimates the solar potential of residential buildings using high quality aerial data. That is, it estimates the potential solar energy (and associated financial savings) that can be captured by buildings if solar panels were to be installed on their roofs. Unfortunately its coverage is limited by the lack of high resolution digital surface map (DSM) data. We present a deep learning approach that bridges this gap by enhancing widely available low-resolution data, thereby dramatically increasing the coverage of Sunroof. We also present some ongoing efforts to potentially improve accuracy even further by replacing certain algorithmic components of Sunroof’s processing pipeline with deep learning. Authors: Ross Goroshin (Google); Carl Elkin (Google)	Power & Energy Computer Vision & Remote Sensing
ICLR 2023	Improving extreme weather events detection with light-weight neural networks (Papers Track) Abstract and authors: (click to expand) Abstract: To advance automated detection of extreme weather events, which are increasing in frequency and intensity with climate change, we explore modifications to a novel light-weight Context Guided convolutional neural network architecture trained for semantic segmentation of tropical cyclones and atmospheric rivers in climate data. Our primary focus is on tropical cyclones, the most destructive weather events, for which current models show limited performance. We investigate feature engineering, data augmentation, learning rate modifications, alternative loss functions, and architectural changes. In contrast to previous approaches optimizing for intersection over union, we specifically seek to improve recall to penalize under-counting and prioritize identification of tropical cyclones. We report success through the use of weighted loss functions to counter class imbalance for these rare events. We conclude with directions for future research on extreme weather events detection, a crucial task for prediction, mitigation, and equitable adaptation to the impacts of climate change. Authors: Romain Lacombe (Stanford University); Hannah Grossman (Stanford); Lucas P Hendren (Stanford University); David Ludeke (Stanford University)	Extreme Weather Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2023	CaML: Carbon Footprinting of Products with Zero-Shot Semantic Text Similarity (Papers Track) Abstract and authors: (click to expand) Abstract: Estimating the embodied carbon in products is a key step towards understanding their impact, and undertaking mitigation actions. Precise carbon attribution is challenging at scale, requiring both domain expertise and granular supply chain data. As a first-order approximation, standard reports use Economic Input-Output based Life Cycle Assessment (EIO-LCA) which estimates carbon emissions per dollar at an industry sector level using transactions between different parts of the economy. For EIO-LCA, an expert needs to map each product to one of upwards of 1000 potential industry sectors. We present CaML, an algorithm to automate EIO-LCA using semantic text similarity matching by leveraging the text descriptions of the product and the industry sector. CaML outperforms the previous manually intensive method, yielding a MAPE of 22% with no domain labels. Authors: Bharathan Balaji (Amazon); Venkata Sai Gargeya Vunnava (amazon); Geoffrey Guest (Amazon); Jared Kramer (Amazon)	Natural Language Processing Supply Chains
ICLR 2023	Graph Neural Network Generated Metal-Organic Frameworks for Carbon Capture (Proposals Track) Abstract and authors: (click to expand) Abstract: The level of carbon dioxide (CO2) in our atmosphere is rapidly rising and is projected to double today‘s levels to reach 1,000 ppm by 2100 under certain scenarios, primarily driven by anthropogenic sources. Technology that can capture CO2 from anthropogenic sources, remove from atmosphere and sequester it at the gigaton scale by 2050 is required stop and reverse the impact of climate change. Metal-organic frameworks (MOFs) have been a promising technology in various applications including gas separation as well as CO2 capture from point-source flue gases or removal from the atmosphere. MOFs offer unmatched surface area through their highly porous crystalline structure and MOF technology has potential to become a leading adsorption-based CO2 separation technology providing high surface area, structure stability and chemical tunability. Due to its complex structure, MOF crystal structure (atoms and bonds) cannot be easily represented in tabular format for machine learning (ML) applications whereas graph neural networks (GNN) have already been explored in representation of simpler chemical molecules. In addition to difficulty in MOF data representation, an infinite number of combinations can be created for MOF crystals, which makes ML applications more suitable to alleviate dependency on subject matter experts (SME) than conventional computational methods. In this work, we propose training of GNNs in variational autoencoder (VAE) setting to create an end-to-end workflow for the generation of new MOF crystal structures directly from the data within the crystallographic information files (CIFs) and conditioned by additional CO2 performance values. Authors: Zikri Bayraktar (Schlumberger Doll Research); Shahnawaz Molla (Schlumberger Doll Research); Sharath Mahavadi (Schlumberger Doll Research)	Generative Modeling Chemistry & Materials
ICLR 2023	Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects (Papers Track) Abstract and authors: (click to expand) Abstract: Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere’s ability to dilute air pollutants, Air Stagnation Index (ASI) has been introduced as an important meteorological index. Therefore, making long-lead ASI forecasts is vital to make plans in advance for air quality management. In this study, we found that autumn Niño indices derived from sea surface temperature (SST) anomalies show a negative correlation with wintertime ASI in southern China, offering prospects for a prewinter forecast. We developed an LSTM-based model to predict the future wintertime ASI. Results demonstrated that multivariate inputs (past ASI and Niño indices) achieve better forecast performance than univariate input (only past ASI). The model achieves a correlation coefficient of 0.778 between the actual and predicted ASI, exhibiting a high degree of consistency. Authors: Chenhong Zhou (Hong Kong Baptist University); Xiaorui Zhang (Hong Kong Baptist University); Meng Gao (Hong Kong Baptist University); Shanshan Liu (University of science and technology of China); Yike Guo (Hong Kong University of Science and Technology); Jie Chen (Hong Kong Baptist University)	Climate Science & Modeling Extreme Weather Time-series Analysis
ICLR 2023	Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions (Papers Track) Abstract and authors: (click to expand) Abstract: Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance. Many global regions, however, have limited streamflow observations to guide evidence-based management strategies. In this paper, we propose an attention-based domain adaptation streamflow forecaster for data-sparse regions. Our approach leverages the hydrological characteristics of a data-rich source domain to induce effective 24hr lead-time streamflow prediction in a data-constrained target domain. Specifically, we employ a deep-learning framework leveraging domain adaptation techniques to simultaneously train streamflow predictions and discern between both domains using an adversarial method. Experiments against baseline cross-domain forecasting models show improved performance for 24hr lead-time streamflow forecasting. Authors: Roland R Oruche (University of Missouri-Columbia); Fearghal O'Donncha (IBM Research)	Meta- and Transfer Learning Disaster Management and Relief Time-series Analysis
ICLR 2023	Predicting Cycling Traffic in Cities: Is bike-sharing data representative of the cycling volume? (Proposals Track) Abstract and authors: (click to expand) Abstract: A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, we predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems in addition to weather, vacation, infrastructure, and socioeconomic indicators. To reach a high prediction accuracy given the diverse data, we make use of machine learning techniques. Our goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Results indicate that bike-sharing data are valuable to improve the predictive performance, especially in cases with high outliers, and help generalize the models to new locations. Authors: Silke K. Kaiser (Hertie School)	Transportation
ICLR 2023	Unsupervised machine learning techniques for multi-model comparison: A case study on Antarctic Intermediate Water in CMIP6 models (Papers Track) Abstract and authors: (click to expand) Abstract: The Climate Model Intercomparison Project provides access to ensembles of model experiments that are widely used to better understand past, present, and future climate changes. In this study, we use Principal Component Analysis and K-means and hierarchical clustering techniques to guide identification of models in the CMIP6 dataset that are best suited for specific modelling objectives. An example is discussed here that focuses on how CMIP6 models reproduce the physical properties of Antarctic Intermediate Water, a key feature of the global oceanic circulation and of the ocean-climate system, noting that the tools and methods introduced here can readily be extended to the analysis of other features and regions. Authors: Ophelie Meuriot (Imperial College London); Yves Plancherel (Imperial College London); Veronica Nieves (University of Valencia)	Climate Science & Modeling Unsupervised & Semi-Supervised Learning
ICLR 2023	An automatic mobile approach for Tree DBH Estimation Using a Depth Map and a Regression Convolutional Neural Network (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon credit programs finance projects to reduce emissions, remove pollutants, improve livelihoods, and protect natural ecosystems. Ensuring the quality and integrity of such projects is essential to their success. One of the most important variables used in nature-based solutions to measure carbon sequestration is the diameter at breast height (DBH) of trees. In this paper, we propose an automatic mobile computer vision method to estimate the DBH of a tree using a single depth map on a smartphone, along with our created dataset DepthMapDBH2023. We successfully demonstrated that this dataset paired with a lightweight regression convolutional neural network is able to accurately estimate the DBH of trees distinct in appearance, shape, number of tree forks, tree density and crowding, and vine presence. Automation of these measurements will help crews in the field who are collecting data for forest inventories. Gathering as much on-the-ground data as possible is required to ensure the transparency of carbon credit projects. Access to high-quality datasets of manual measurements helps improve biomass models which are widely used in the field of ecological simulation. The code used in this paper will be publicly available on Github and the dataset on Kaggle. Authors: Margaux Masson-Forsythe (Earthshot Labs); Margaux Masson-Forsythe (Earthshot Labs)	Computer Vision & Remote Sensing Carbon Capture & Sequestration Climate Science & Modeling Forests Land Use
ICLR 2023	Safe Multi-Agent Reinforcement Learning for Price-Based Demand Response (Papers Track) Abstract and authors: (click to expand) Abstract: Price-based demand response (DR) enables households to provide the flexibility required in power grids with a high share of volatile renewable energy sources. Multi-agent reinforcement learning (MARL) offers a powerful, decentralized decision-making tool for autonomous agents participating in DR programs. Unfortunately, MARL algorithms do not naturally allow one to incorporate safety guarantees, preventing their real-world deployment. To meet safety constraints, we propose a safety layer that minimally adjusts each agent's decisions. We investigate the influence of using a reward function that reflects these safety adjustments. Results show that considering safety aspects in the reward during training improves both convergence speed and performance of the MARL agents in the investigated numerical experiments. Authors: Hannah Markgraf (Technical University of Munich); Matthias Althoff (Technical University of Munich)	Reinforcement Learning Buildings Power & Energy
ICLR 2023	BurnMD: A Fire Projection and Mitigation Modeling Dataset (Papers Track) Abstract and authors: (click to expand) Abstract: Today's fire projection modeling tools struggle to keep up with the rapid rate and increasing severity of climate change, leaving disaster managers dependent on tools which are increasingly unrepresentative of complex interactions between fire behavior, environmental conditions, and various mitigation options. This has consequences for equitably minimizing wildfire risks to life, property, ecology, cultural heritage, and public health. Fortunately, decades of data exist for fuel populations, weather conditions, and outcomes of significant fires in the American West and globally. The fire management community faces a lack of data standardization and validation among many competing fire models. Likewise, the machine learning community lacks curated datasets and benchmarks to develop solutions necessary to generate impact in this space. We present a novel dataset composed of 308 medium sized fires from the years 2018-2021, complete with both time series airborne based inference and ground operational estimation of fire extent, and operational mitigation data such as control line construction. As the first large wildfire dataset with mitigation information, Burn Mitigation Dataset (BurnMD) will help advance fire projection modeling, fire risk modeling, and AI generated land management policies. Authors: Marissa Dotter (MITRE Corporation)	Forests Climate Science & Modeling Computer Vision & Remote Sensing Time-series Analysis
ICLR 2023	MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids (Papers Track) Abstract and authors: (click to expand) Abstract: Integration of variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability. This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids. The framework addresses the challenges above: it seeks to optimize the usage of available resources by minimizing the carbon footprint while benefiting all stakeholders. The proposed architecture consists of three layers of agents, each pursuing different objectives. The first layer, comprised of prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon impact while balancing the consumption and production of both renewable and conventional energy. This framework also takes into account fluctuations in energy demand and supply. Authors: Nicolas M Cuadrado (MBZUAI); Roberto Alejandro Gutierrez Guillen (MBZUAI); Yongli Zhu (Texas A&M University); Martin Takac (Mohamed bin Zayed University of Artificial Intelligence)	Power & Energy Reinforcement Learning
ICLR 2023	Improving a Shoreline Forecasting Model with Symbolic Regression (Papers Track) Abstract and authors: (click to expand) Abstract: Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution's ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones. Authors: Mahmoud AL NAJAR (Laboratory of Spatial Geophysics and Oceanography Studies); Rafael ALMAR (Laboratory of Spatial Geophysics and Oceanography Studies); Erwin BERGSMA (CNES); Jean-Marc DELVIT (CNES); Dennis Wilson (ISAE)	Interpretable ML Earth Observation & Monitoring Extreme Weather Hybrid Physical Models Time-series Analysis
ICLR 2023	Remote Control: Debiasing Remote Sensing Predictions for Causal Inference (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding and properly estimating the impacts of environmental interventions is of critical importance as we work towards achieving global climate goals. Remote sensing has become an essential tool for evaluating when and where climate policies have positive impacts on factors like greenhouse gas emissions and carbon sequestration. However, when machine learning models trained to predict outcomes using remotely sensed data simply minimize a standard loss function, the predictions that they generate can produce biased estimates in downstream causal inference. If prediction error in the outcome variable is correlated with policy variables or important confounders, as is the case for many widely used remote sensing data sets, estimates of the causal impacts of policies can be biased. In this paper, we demonstrate how this bias can arise, and we propose the use of an adversarial debiasing model (Zhang, Lemoine, and Mitchell 2018) in order to correct the issue when using satellite data to generate machine learning predictions for use in causal inference. We apply this method to a case study of the relationship between roads and tree cover in West Africa, where our results indicate that adversarial debiasing can recover a much more accurate estimate of the parameter of interest compared to when the standard approach is used. Authors: Matthew Gordon (Yale); Megan Ayers (Yale University); Eliana Stone (Yale School of the Environment); Luke C Sanford (Yale School of the Environment)	Forests Computer Vision & Remote Sensing Land Use
ICLR 2023	A simplified machine learning based wildfire ignition model from insurance perspective (Papers Track) Abstract and authors: (click to expand) Abstract: In the context of climate change, wildfires are becoming more frequent, intense, and prolonged in the western US, particularly in California. Wildfires cause catastrophic socio-economic losses and are projected to worsen in the near future. Inaccurate estimates of fire risk put further pressure on wildfire (re)insurance and cause many homes to lose wildfire insurance coverage. Efficient and effective prediction of fire ignition is one step towards better fire risk assessment. Here we present a simplified machine learning-based fire ignition model at yearly scale that is well suited to the use case of one-year term wildfire (re)insurance. Our model yields a recall, precision, and the area under the precision-recall curve of 0.69, 0.86 and 0.81, respectively, for California, and significantly higher values of 0.82, 0.90 and 0.90, respectively, for the populated area, indicating its good performance. In addition, our model feature analysis reveals that power line density, enhanced vegetation index (EVI), vegetation optical depth (VOD), and distance to the wildland-urban interface stand out as the most important features determining ignitions. The framework of this simplified ignition model could easily be applied to other regions or genesis of other perils like hurricane, and it paves the road to a broader and more affordable safety net for homeowners. Authors: Yaling Liu (OurKettle Inc); Son Le (OurKettle Inc.); Yufei Zou (Our Kettle, Inc.); mojtaba Sadgedhi (OurKettle Inc.); Yang Chen (University of California, Irvine); Niels Andela (BeZero Carbon); Pierre Gentine (Columbia University)	Earth Observation & Monitoring Climate Science & Modeling Societal Adaptation & Resilience Interpretable ML
ICLR 2023	Nested Fourier Neural Operator for Basin-Scale 4D CO2 Storage Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. However, such modeling is very challenging at scale due to the high computational costs of existing numerical methods. This challenge leads to significant uncertainty in evaluating storage opportunities which can delay the pace of global CCS deployments. We introduce a machine-learning approach for dynamic basin-scale modeling that speeds up flow prediction nearly 700,000 times compared to existing methods. Our framework, Nested Fourier Neural Operator (FNO), provides a general-purpose simulator alternative under diverse reservoir conditions, geological heterogeneity, and injection schemes. It enables unprecedented real-time high-fidelity modeling to support decision-making in basin-scale CCS projects. Authors: Gege Wen (Stanford University)	Carbon Capture & Sequestration Climate Science & Modeling
ICLR 2023	SEA LEVEL PROJECTIONS WITH MACHINE LEARNING USING ALTIMETRY AND CLIMATE MODEL ENSEMBLES (Papers Track) Abstract and authors: (click to expand) Abstract: Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year). With almost three decades of observations, we can now investigate the contributions of anthropogenic climate-change signals such as greenhouse gases, aerosols, and biomass burning in this rising sea level. We use machine learning (ML) to investigate future patterns of sea level change. To understand the extent of contributions from the climate-change signals, and to help in forecasting sea level change in the future, we turn to climate model simulations. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections at a 2-degree resolution spatial grid, 30 years into the future. We train fully connected neural networks (FCNNs) to predict altimeter values through a non-linear fusion of the climate model hindcasts (for 1993-2019). The learned FCNNs are then applied to future climate model projections to predict future sea level patterns. We propose segmenting our spatial dataset into meaningful clusters and show that clustering helps to improve predictions of our ML model. Authors: Saumya Sinha (University of Colorado, Boulder); John Fasullo (NCAR); R. Steven Nerem (Univesity of Colorado, Boulder); Claire Monteleoni (University of Colorado Boulder)	Climate Science & Modeling Oceans & Marine Systems
ICLR 2023	Global-Local Policy Search and Its Application in Grid-Interactive Building Control (Papers Track) Abstract and authors: (click to expand) Abstract: As the buildings sector represents over 70% of the total U.S. electricity consumption, it offers a great amount of untapped demand-side resources to tackle many critical grid-side problems and improve the overall energy system's efficiency. To help make buildings grid-interactive, this paper proposes a global-local policy search method to train a reinforcement learning (RL) based controller which optimizes building operation during both normal hours and demand response (DR) events. Experiments on a simulated five-zone commercial building demonstrate that by adding a local fine-tuning stage to the evolution strategy policy training process, the control costs can be further reduced by 7.55% in unseen testing scenarios. Baseline comparison also indicates that the learned RL controller outperforms a pragmatic linear model predictive controller (MPC), while not requiring intensive online computation. Authors: Xiangyu Zhang (National Renewable Energy Laboratory); Yue Chen (National Renewable Energy Laboratory); Andrey Bernstein (NREL)	Buildings Power & Energy Reinforcement Learning
ICLR 2023	Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models (Papers Track) Abstract and authors: (click to expand) Abstract: Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019). To address this issue, we have created an energy estimation pipeline, which allows practitioners to estimate the energy needs of their models in advance, without actually running or training them. We accomplished this, by collecting high-quality energy data and building a first baseline model, capable of predicting the energy consumption of DL models by accumulating their estimated layer-wise energies. Authors: Johannes Getzner (Technical University of Munich); Bertrand Charpentier (Technical University of Munich); Stephan Günnemann (Technical University of Munich)	Power & Energy Data Mining
ICLR 2023	Topology Estimation from Voltage Edge Sensing for Resource-Constrained Grids (Papers Track) Abstract and authors: (click to expand) Abstract: Electric grids are the conduit for renewable energy delivery and will play a crucial role in mitigating climate change. To do so successfully in resource-constrained low- and middle-income countries (LMICs), increasing operational efficiency is key. Such efficiency demands evolving knowledge of the grid’s state, of which topology---how points on the network are physically connected---is fundamental. In LMICs, knowledge of distribution topology is limited and established methods for topology estimation rely on expensive sensing infrastructure, such as smart meters or PMUs, that are inaccessible at scale. This paper lays the foundation for topology estimation from more accessible data: outlet-level voltage magnitude measurements. It presents a graph-based algorithm and explanatory visualization using the Fielder vector for estimating and communicating topological proximity from this data. We demonstrate the method on a real dataset collected in Accra, Ghana, thus opening the possibility of globally accessible, cutting-edge grid monitoring through non-traditional sensing strategies coupled with ML. Authors: Mohini S Bariya (nLine); Genevieve Flaspohler (nLine)	Unsupervised & Semi-Supervised Learning Climate Justice Time-series Analysis
ICLR 2023	Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training (Papers Track) Abstract and authors: (click to expand) Abstract: Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networks (DNNs). Answering the call for sustainability, conventional solutions have attempted to move training jobs to locations or time frames with lower carbon intensity. However, moving jobs to other locations may not always be feasible due to large dataset sizes or data regulations. Moreover, postponing training can negatively impact application service quality because the DNNs backing the service are not updated in a timely fashion. In this work, we present a practical solution that reduces the carbon footprint of DNN training without migrating or postponing jobs. Specifically, our solution observes real-time carbon intensity shifts during training and controls the energy consumption of GPUs, thereby reducing carbon footprint while maintaining training performance. Furthermore, in order to proactively adapt to shifting carbon intensity, we propose a lightweight machine learning algorithm that predicts the carbon intensity of the upcoming time frame. Our solution, Chase, reduces the total carbon footprint of training ResNet-50 on ImageNet by 13.6% while only increasing training time by 2.5%. Authors: Zhenning Yang (University of Michigan, Ann Arbor); Luoxi Meng (University of Michigan, Ann Arbor); Jae-Won Chung (University of Michigan, Ann Arbor); Mosharaf Chowdhury (University of Michigan, Ann Arbor)	Time-series Analysis
ICLR 2023	SOLAR PANEL MAPPING VIA ORIENTED OBJECT DETECTION (Papers Track) Abstract and authors: (click to expand) Abstract: Maintaining the integrity of solar power plants is a vital component in dealing with the current climate crisis. This process begins with analysts creating a de- tailed map of a plant with the coordinates of every solar panel, making it possible to quickly locate and mitigate potential faulty solar panels. However, this task is extremely tedious and is not scalable for the ever increasing capacity of so- lar power across the globe. Therefore, we propose an end-to-end deep learning framework for detecting individual solar panels using a rotated object detection architecture. We evaluate our approach on a diverse dataset of solar power plants collected from across the United States and report a mAP score of 83.3%. Authors: Conor Wallace (DroneBase); Isaac Corley (University of Texas at San Antonio); Jonathan Lwowski (DroneBase)	Computer Vision & Remote Sensing
ICLR 2023	Disentangling observation biases to monitor spatio-temporal shifts in species distributions (Proposals Track) Abstract and authors: (click to expand) Abstract: The accelerated pace of environmental change due to anthropogenic activities makes it more important than ever to understand current and future ecosystem dynamics at a global scale. Species observations stemming from citizen science platforms are increasingly leveraged to gather information about the geographic distributions of many species. However, their usability is limited by the strong biases inherent to these community-driven efforts. These biases in the sampling effort are often treated as noise that has to be compensated for. In this project, we posit that better modelling the sampling effort (including the usage of the different platforms across countries, local accessibility, attractiveness of the location for platform users, affinity of different user groups for different species, etc.) is the key towards improving Species Distribution Models (SDM) using observations from citizen science platforms, thus opening up the possibility of leveraging them to monitor changes in species distributions and population densities. Authors: Diego Marcos (Inria); Christophe Botella (); Ilan Havinga (Wageningen University); Dino Ienco (INRAE); Cassio F. Dantas (TETIS, INRAE, Univ Montpellier); Pierre Alliez (INRIA Sophie-Antipolis, France); Alexis Joly (INRIA, FR)	Causal & Bayesian Methods Earth Observation & Monitoring Computer Vision & Remote Sensing Hybrid Physical Models
ICLR 2023	Mapping global innovation networks around clean energy technologies (Proposals Track) Abstract and authors: (click to expand) Abstract: Reaching net zero emissions requires rapid innovation and scale-up of clean tech. In this context, clean tech innovation networks (CTINs) can play a crucial role by pooling necessary resources and competences and enabling knowledge transfers between different actors. However, existing evidence on CTINs is limited due to a lack of comprehensive data. Here, we develop a machine learning framework to identify CTINs from announcements on social media to map the global CTIN landscape. Specifically, we classify the social media announcements regarding the type of technology (e.g., hydrogen, solar), interaction type (e.g., equity investment, R\&D collaboration), and status (e.g., commencement, update). We then extract referenced organizations via entity recognition. Thereby, we generate a large-scale dataset of CTINs across different technologies, countries, and over time. This allows us to compare characteristics of CTINs, such as the geographic proximity of actors, and to investigate the association between network evolution and technology innovation and diffusion. As a direct implication, our work helps policy makers to promote CTINs by identifying current barriers and needs. Authors: Malte Toetzke (ETH Zurich); Francesco Re (ETH Zurich); Benedict Probst (ETH Zurich); Stefan Feuerriegel (LMU Munich); Laura Diaz Anadon (University of Cambridge); Volker Hoffmann (ETH Zurich)	Climate Finance & Economics Supply Chains Natural Language Processing
ICLR 2023	Widespread increases in future wildfire risk to global forest carbon offset projects revealed by explainable AI (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon offset programs are critical in the fight against climate change. One emerging threat to the long-term stability and viability of forest carbon offset projects is wildfires, which can release large amounts of carbon and limit the efficacy of associated offsetting credits. However, analysis of wildfire risk to forest carbon projects is challenging because existing models for forecasting long-term fire risk are limited in predictive accuracy. Therefore, we propose an explainable artificial intelligence (XAI) model trained on 7 million global satellite wildfire observations. Validation results suggest substantial potential for high resolution, enhanced accuracy projections of global wildfire risk, and the model outperforms the U.S. National Center for Atmospheric Research's leading fire model. Applied to a collection of 190 global forest carbon projects, we find that fire exposure is projected to increase 55% [37-76%] by 2080 under a mid-range scenario (SSP2-4.5). Our results indicate the large wildfire carbon project damages seen in the past decade are likely to become more frequent as forests become hotter and drier. In response, we hope the model can support wildfire managers, policymakers, and carbon market analysts to preemptively quantify and mitigate long-term permanence risks to forest carbon projects. Authors: Tristan Ballard (Sust Inc); Gopal Erinjippurath (Sust Global); Matthew W Cooper (Sust Global); Chris Lowrie (Sust Global)	Climate Finance & Economics Forests Interpretable ML Land Use
ICLR 2023	A High-Resolution, Data-Driven Model of Urban Carbon Emissions (Papers Track) Best Pathway to Impact Abstract and authors: (click to expand) Abstract: Cities represent both a fundamental contributor to greenhouse (GHG) emissions and a catalyst for climate action. Many global cities have outlined sustainability and climate change mitigation plans, focusing on energy efficiency, shifting away from fossil fuels, and prioritizing environmental and social justice. To achieve broad-based and equitable carbon emissions reductions and sustainability goals, new data-driven methodologies are needed to identify and target efficiency and carbon reduction opportunities in the built environment at the building, neighborhood, and city-scale. Our methodology integrates data from numerous data sources and develops data-driven and physical models of energy use and carbon emissions from buildings and transportation to generate a high spatiotemporal resolution model of urban greenhouse gas emissions. The method and data tool are designed to support city leaders and urban policymakers with an unprecedented view of localized carbon emissions to enable data-driven and evidenced-based climate action. Authors: Bartosz Bonczak (New York University); Boyeong Hong (New York University); Constantine E. Kontokosta (New York University)	Hybrid Physical Models Climate Science & Modeling Data Mining
ICLR 2023	ClimaX: A foundation model for weather and climate (Papers Track) Abstract and authors: (click to expand) Abstract: Recent data-driven approaches based on machine learning aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of currently used physics-informed numerical models for weather and climate modeling. We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatiotemporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute and data while maintaining general utility. ClimaX is pre-trained with a self-supervised learning objective on climate datasets derived from CMIP6. The pretrained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatiotemporal scales unseen during pretraining. Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections. Authors: Tung Nguyen (University of California, Los Angeles); Johannes Brandstetter (Microsoft Research); Ashish Kapoor (Microsoft); Jayesh Gupta (Microsoft Research); Aditya Grover (UCLA)	Climate Science & Modeling Meta- and Transfer Learning
ICLR 2023	Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics (Papers Track) Abstract and authors: (click to expand) Abstract: Finding a balance between collaboration and competition is crucial for artificial agents in many real-world applications. We investigate this using a Multi-Agent Reinforcement Learning (MARL) setup on the back of a high-impact problem. The accumulation and yearly growth of plastic in the ocean cause irreparable damage to many aspects of oceanic health and the marina system. To prevent further damage, we need to find ways to reduce macroplastics from known plastic patches in the ocean. Here we propose a Graph Neural Network (GNN) based communication mechanism that increases the agents' observation space. In our custom environment, agents control a plastic collecting vessel. The communication mechanism enables agents to develop a communication protocol using a binary signal. While the goal of the agent collective is to clean up as much as possible, agents are rewarded for the individual amount of macroplastics collected. Hence agents have to learn to communicate effectively while maintaining high individual performance. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show communication enables collaboration and increases collective performance significantly. This means agents have learned the importance of communication and found a balance between collaboration and competition. Authors: Philipp D Siedler (Aleph Alpha)	Reinforcement Learning Oceans & Marine Systems
ICLR 2023	Sub-seasonal to seasonal forecasts through self-supervised learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Sub-seasonal to seasonal (S2S) weather forecasts are an important decision- making tool that informs economical and logistical planning in agriculture, energy management, and disaster mitigation. They are issued on time scales of weeks to months and differ from short-term weather forecasts in two important ways: (i) the dynamics of the atmosphere on these timescales can be described only statistically and (ii) these dynamics are characterized by large-scale phenomena in both space and time. While deep learning (DL) has shown promising results in short-term weather forecasting, DL-based S2S forecasts are challenged by comparatively small volumes of available training data and large fluctuations in predictability due to atmospheric conditions. In order to develop more reliable S2S predictions that leverage current advances in DL, we propose to utilize the masked auto-encoder (MAE) framework to learn generic representations of large-scale atmospheric phenomena from high resolution global data. Besides exploring the suitability of the learned representations for S2S forecasting, we will also examine whether they account for climatic phenomena (e.g., the Madden-Julian Oscillation) that are known to increase predictability on S2S timescales. Authors: Jannik Thuemmel (University of Tuebingen); Felix Strnad (Potsdam Institute for Climate Impact Research); Jakob Schlör (Eberhard Karls Universität Tübingen); Martin V. Butz (University of Tübingen); Bedartha Goswami (University of Tübingen)	Unsupervised & Semi-Supervised Learning Climate Science & Modeling Extreme Weather Interpretable ML
ICLR 2023	Mining Effective Strategies for Climate Change Communication (Papers Track) Abstract and authors: (click to expand) Abstract: With the goal of understanding effective strategies to communicate about climate change, we build interpretable models to rank tweets related to climate change with respect to the engagement they generate. Our models are based on the Bradley-Terry model of pairwise comparison outcomes and use a combination of the tweets’ topic and metadata features to do the ranking. To remove confounding factors related to author popularity and minimise noise, they are trained on pairs of tweets that are from the same author and around the same time period and have a sufficiently large difference in engagement. The models achieve good accuracy on a held-out set of pairs. We show that we can interpret the parameters of the trained model to identify the topic and metadata features that contribute to high engagement. Among other observations, we see that topics related to climate projections, human cost and deaths tend to have low engagement while those related to mitigation and adaptation strategies have high engagement. We hope the insights gained from this study will help craft effective climate communication to promote engagement, thereby lending strength to efforts to tackle climate change. Authors: Aswin Suresh (EPFL); Lazar Milikic (EPFL); Francis Murray (EPFL); Yurui Zhu (EPFL); Matthias Grossglauser (École Polytechnique Fédérale de Lausanne (EPFL))	Natural Language Processing Behavioral and Social Science Public Policy Societal Adaptation & Resilience Data Mining Interpretable ML Unsupervised & Semi-Supervised Learning
ICLR 2023	Graph-Based Deep Learning for Sea Surface Temperature Forecasts (Papers Track) Abstract and authors: (click to expand) Abstract: Sea surface temperature (SST) forecasts help with managing the marine ecosystem and the aquaculture impacted by anthropogenic climate change. Numerical dynamical models are resource intensive for SST forecasts; machine learning (ML) models could reduce high computational requirements and have been in the focus of the research community recently. ML models normally require a large amount of data for training. Environmental data are collected on regularly-spaced grids, so early work mainly used grid-based deep learning (DL) for prediction. However, both grid data and the corresponding DL approaches have inherent problems. As geometric DL has emerged, graphs as a more generalized data structure and graph neural networks (GNNs) have been introduced to the spatiotemporal domains. In this work, we preliminarily explored graph re-sampling and GNNs for global SST forecasts, and GNNs show better one month ahead SST prediction than the persistence model in most oceans in terms of root mean square errors. Authors: Ding Ning (University of Canterbury); Varvara Vetrova (University of Canterbury); Karin Bryan (University of Waikato)	Data Mining Climate Science & Modeling Oceans & Marine Systems Time-series Analysis
ICLR 2023	Activity-Based Recommendations for the Reduction of CO2 Emissions in Private Households (Papers Track) Abstract and authors: (click to expand) Abstract: This paper proposes an activity prediction framework for a multi-agent recommendation system to tackle the energy-efficiency problem in residential buildings. Our system generates an activity-shifting schedule based on the social practices from the users’ domestic life. We further provide a utility option for the recommender system to focus on saving CO2 emissions or energy costs, or both. The empirical results show that while focusing on the reduction of CO2 emissions, the system provides an average of 12% of emission savings and 7% of electricity cost savings. When concentrating on energy costs, 6% of emission savings and 20% of electricity cost savings are possible for the studied households. Authors: Alona Zharova (Humboldt University of Berlin); Laura Löschmann (Humboldt University of Berlin)	Recommender Systems Buildings Power & Energy
ICLR 2023	Data-driven mean-variability optimization of PV portfolios with automatic differentiation (Papers Track) Abstract and authors: (click to expand) Abstract: Increasing PV capacities has a crucial role to reach carbon-neutral energy systems. To promote PV expansion, policy designs have been developed which rely on energy yield maximization to increase the total PV energy supply in energy systems. Focusing on yield maximization, however, ignores negative side-effects such as an increased variability due to similar-orientated PV systems at clustered regions. This can lead to costly ancillary services and thereby reduces the acceptance of renewable energy. This paper suggests to rethink PV portfolio designs by deriving mean-variability hedged PV portfolios with smartly orientated tilt and azimuth angles. Based on a data-driven method inspired from modern portfolio theory, we formulate the problem as a biobjective, non-convex optimization problem which is solved based on automatically differentiating the physical PV conversion model subject to individual tilt and azimuth angles. To illustrate the performance of the proposed method, a case study is designed to derive efficient frontiers in the mean-variability spectrum of Germany's PV portfolio based on representative grid points. The proposed method allows decision-makers to hedge between variability and yield in PV portfolio design decisions. This is the first study highlighting the problem of ignoring variability in PV portfolio expansion schemes and introduces a way to tackle this issue using modern methods inspired by Machine Learning. Authors: Matthias Zech (German Aerospace Center (DLR), Institute of Networked Energy Systems); Lueder von Bremen (German Aerospace Center (DLR), Institute of Networked Energy Systems)	Power & Energy Climate Finance & Economics Interpretable ML
ICLR 2023	DiffESM: Conditional Emulation of Earth System Models with Diffusion Models (Papers Track) Abstract and authors: (click to expand) Abstract: Earth System Models (ESMs) are essential tools for understanding the impact of human actions on Earth's climate. One key application of these models is studying extreme weather events, such as heat waves or dry spells, which have significant socioeconomic and environmental consequences. However, the computational demands of running a sufficient number of simulations to analyze the risks are often prohibitive. In this paper we demonstrate that diffusion models -- a class of generative deep learning models -- can effectively emulate the spatio-temporal trends of ESMs under previously unseen climate scenarios, while only requiring a small fraction of the computational resources. We present a diffusion model that is conditioned on monthly averages of temperature or precipitation on a 96x96 global grid, and produces daily values that are both realistic and consistent with those averages. Our results show that the output from our diffusion model closely matches the spatio-temporal behavior of the ESM it emulates in terms of the frequency of phenomena such as heat waves, dry spells, or rainfall intensity. Authors: Seth Bassetti (Western Washington University); Brian Hutchinson (Western Washington University); Claudia Tebaldi (Joint Global Change Research Institute); Ben Kravitz (Indiana University)	Climate Science & Modeling Generative Modeling
ICLR 2023	Emission-Constrained Optimization of Gas Systems with Input-Convex Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Planning optimization of gas networks under emission constraints prioritizes gas supply with the smallest emission footprint. As this problem includes complex gas flow physical laws, standard optimization solvers cannot guarantee convergence to a feasible solution, especially under strict emission constraints. To address this issue, we develop an input-convex neural network (ICNN) aided optimization routine which incorporates a set of trained ICNNs approximating the gas flow equations with high precision. Numerical tests on the Belgium gas network demonstrate that the ICNN-aided optimization dominates non-convex and relaxation-based solvers, with larger optimality gains pertaining to stricter emission targets. Authors: Vladimir Dvorkin (Massachusetts Institute of Technology); Samuel C Chevalier (Technical University of Denmark); Spyros Chatzivasileiadis (Technical University of Denmark)	Power & Energy Supply Chains
ICLR 2023	Deep ensembles to improve uncertainty quantification of statistical downscaling models under climate change conditions (Papers Track) Abstract and authors: (click to expand) Abstract: Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. By better capturing uncertainty, statistical downscaling models allow for superior planning against extreme weather events, a source of various negative social and economic impacts. Since no observational future data exists, we rely on a pseudo reality experiment to assess the suitability of deep ensembles for quantifying the uncertainty of climate change projections. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change. Authors: Jose González-Abad (Instituto de Fı́sica de Cantabria (IFCA), CSIC-Universidad de Cantabria); Jorge Baño-Medina (Institute of Physics of Cantabria)	Uncertainty Quantification & Robustness Climate Science & Modeling
ICLR 2023	Bayesian Inference of Severe Hail in Australia (Papers Track) Abstract and authors: (click to expand) Abstract: Severe hailstorms are responsible for some of the most costly insured weather events in Australia and can cause significant damage to homes, businesses, and agriculture. However their response to climate change remains uncertain, in large part due to the challenges of observing severe hailstorms. We propose a novel Bayesian approach which explicitly models known biases and uncertainties of current hail observations to produce more realistic estimates of severe hail risk from existing observations. Training this model on data from south-east Queensland, Australia, suggests that previous analyses of severe hail that did not account for this uncertainty may produce poorly calibrated risk estimates. Preliminary evaluation on withheld data confirms that our model produces well-calibrated probabilities and is applicable out of sample. Whilst developed for hail, we highlight also the generality of our model and its potential applications to other severe weather phenomena and areas of climate change adaptation and mitigation. Authors: Isabelle C Greco (University of New South Wales); Steven Sherwood (University of New South Wales); Timothy Raupach (University of New South Wales); Gab Abramowitz (University of New South Wales)	Causal & Bayesian Methods Climate Science & Modeling Disaster Management and Relief Earth Observation & Monitoring Extreme Weather Uncertainty Quantification & Robustness
ICLR 2023	Exploring the potential of neural networks for Species Distribution Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent advances in neural networks may have untapped potential in this field. In this work, we compare the performance of multi-layer perceptron (MLP) neural networks to well-established SDM methods on a benchmark dataset spanning 225 species in six geographical regions. We also compare the performance of MLPs trained separately for each species to an equivalent model trained on a set of species and performing multi-label classification. Our results show that MLP models achieve comparable results to state-of-the-art SDM methods, such as MaxEnt. We also find that multi-species MLPs perform slightly better than single-species MLPs. This study indicates that neural networks, along with all their convenient and valuable characteristics, are worth considering for SDMs. Authors: Robin Zbinden (EPFL); Nina van Tiel (EPFL); Benjamin Kellenberger (Yale University); Lloyd H Hughes (EPFL); Devis Tuia (EPFL)	Ecosystems & Biodiversity
ICLR 2023	Modelling Atmospheric Dynamics with Spherical Fourier Neural Operators (Papers Track) Abstract and authors: (click to expand) Abstract: Fourier Neural Operators (FNOs) have established themselves as an efficient method for learning resolution-independent operators in a wide range of scientific machine learning applications. This can be attributed to their ability to effectively model long-range dependencies in spatio-temporal data through computationally ef- ficient global convolutions. However, the use of discrete Fourier transforms (DFTs) in FNOs leads to spurious artifacts and pronounced dissipation when applied to spherical coordinates, due to the incorrect assumption of flat geometry. To ad- dress the issue, we introduce Spherical FNOs (SFNOs), which use the generalized Fourier transform for learning operators on spherical geometries. We demonstrate the effectiveness of the method for forecasting atmospheric dynamics, producing stable auto-regressive results for a simulated time of one year (1,460 steps) while retaining physically plausible dynamics. This development has significant implica- tions for machine learning-based climate dynamics emulation, which could play a crucial role in accelerating our response to climate change. Authors: Boris Bonev (NVIDIA); Thorsten Kurth (Nvidia); Christian Hundt (NVIDIA AI Technology Center); Jaideep Pathak (NVIDIA Corporation); Maximilian Baust (NVIDIA); Karthik Kashinath (NVIDIA); Anima Anandkumar (NVIDIA/Caltech)	Hybrid Physical Models Climate Science & Modeling Extreme Weather
ICLR 2023	Distributed Reinforcement Learning for DC Open Energy Systems (Papers Track) Abstract and authors: (click to expand) Abstract: The direct current open energy system (DCOES) enables the production, storage, and exchange of renewable energy within local communities, which is helpful, especially in isolated villages and islands where centralized power supply is unavailable or unstable. As solar and wind energy production varies in time and space depending on the weather and the energy usage patterns differ for different households, how to store and exchange energy is an important research issue. In this work, we explore the use of deep reinforcement learning (DRL) for adaptive control of energy storage in local batteries and energy sharing through DC grids. We extend the Autonomous Power Interchange System (APIS) emulator from SonyCSL to combine it with reinforcement learning algorithms in each house. We implemented deep Q-network (DQN) and prioritized DQN to dynamically set the parameters of the real-time energy exchange protocol of APIS and tested it using the actual data collected from the DCOES in the faculty houses of Okinawa Institute of Science and Technology (OIST). The simulation results showed that RL agents outperformed the hand-tuned control strategy. Sharing average energy production, storage, and usage within the local community further improved efficiency. The implementation of DRL methods for adaptive energy storage and exchange can help reducing carbon emission and positively impact the climate. Authors: Qiong Huang (Okinawa Institute of Science and Technology Graduate University); Kenji Doya (Okinawa Institute of Science and Technology)	Reinforcement Learning Power & Energy
ICLR 2023	Uncovering the Spatial and Temporal Variability of Wind Resources in Europe: A Web-Based Data-Mining Tool (Papers Track) Abstract and authors: (click to expand) Abstract: We introduce REmap-eu.app, a web-based data-mining visualization tool of the spatial and temporal variability of wind resources. It uses the latest open-access dataset of the daily wind capacity factor in 28 European countries between 1979 and 2019 and proposes several user-configurable visualizations of the temporal and spatial variations of the wind power capacity factor. The platform allows for a deep analysis of the distribution, the cross-country correlation, and the drivers of low wind power events. It offers an easy-to-use interface that makes it suitable for the needs of researchers and stakeholders. The tool is expected to be useful in identifying areas of high wind potential and possible challenges that may impact the large-scale deployment of wind turbines in Europe. Particular importance is given to the visualization of low wind power events and to the potential of cross-border cooperations in mitigating the variability of wind in the context of increasing reliance on weather-sensitive renewable energy sources. Authors: Alban Puech (École Polytechnique); Jesse Read (Ecole Polytechnique)	Data Mining Climate Science & Modeling Power & Energy Time-series Analysis
ICLR 2023	Understanding forest resilience to drought with Shapley values (Proposals Track) Abstract and authors: (click to expand) Abstract: Increases in drought frequency, intensity, and duration due to climate change are threatening forests around the world. Climate-driven tree mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially fine-grained understanding of the site characteristics making forests more resilient to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate drought indices. In this study, we aim to gain a better understanding of the temporal and spatial drivers of drought-induced changes in forest vitality using Shapley values, which allow for the relevance of predictors to be quantified locally. A better understanding of the contribution of meteorological and environmental factors to trees’ response to drought can support forest managers aiming to make forests more climate-resilient. Authors: Stenka Vulova (Technische Universität Berlin); Alby Duarte Rocha (Technische Universität Berlin); Akpona Okujeni (Humboldt-Universität zu Berlin); Johannes Vogel (Freie Universität Berlin); Michael Förster (Technische Universität Berlin); Patrick Hostert (Humboldt-Universität zu Berlin); Birgit Kleinschmit (Technische Universität Berlin)	Forests Earth Observation & Monitoring Climate Science & Modeling Ecosystems & Biodiversity Extreme Weather Interpretable ML
ICLR 2023	Improving the spatial accuracy of extreme tropical cyclone rainfall in ERA5 using deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: We propose a novel method for the bias adjustment and post-processing of gridded rainfall data products. Our method uses U-Net (a deep convolutional neural network) as a backbone, and a novel loss function given by the combination of a pixelwise bias component (Mean Absolute Error) and a spatial accuracy component (Fractions Skill Score). We evaluate the proposed approach by adjusting extreme rainfall from the popular ERA5 reanalysis dataset, using the multi-source observational dataset MSWEP as a target. We focus on a sample of extreme rainfall events induced by tropical cyclones and show that the proposed method significantly reduces both the MAE (by 16\%) and FSS (by 53\%) of ERA5. Authors: Guido Ascenso (Politecnico di Milano); Andrea Ficchì (Politecnico di Milano); Matteo Giuliani (Politecnico di Milano); Leone Cavicchia (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Enrico Scoccimarro (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Andrea Castelletti (Politecnico di Milano)	Extreme Weather Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2023	XAI for transparent wind turbine power curve models (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition. While machine learning (ML) methods have shown significant advantages over parametric, physics-informed approaches, they are often criticized for being opaque "black boxes", which hinders their application in practice. We apply Shapley values, a popular explainable artificial intelligence (XAI) method, and the latest findings from XAI for regression models, to uncover the strategies ML models have learned from operational wind turbine data. Our findings reveal that the trend towards ever larger model architectures, driven by a focus on test set performance, can result in physically implausible model strategies. Therefore, we call for a more prominent role of XAI methods in model selection. Moreover, we propose a practical approach to utilize explanations for root cause analysis in the context of wind turbine performance monitoring. This can help to reduce downtime and increase the utilization of turbines in the field. Authors: Simon Letzgus (Technische Universität Berlin)	Power & Energy Interpretable ML
ICLR 2023	Green AutoML for Plastic Litter Detection (Papers Track) Abstract and authors: (click to expand) Abstract: The world’s oceans are polluted with plastic waste and the detection of it is an important step toward removing it. Wolf et al. (2020) created a plastic waste dataset to develop a plastic detection system. Our work aims to improve the machine learning model by using Green Automated Machine Learning (AutoML). One aspect of Green-AutoML is to search for a machine learning pipeline, while also minimizing the carbon footprint. In this work, we train five standard neural architectures for image classification on the aforementioned plastic waste dataset. Subsequently, their performance and carbon footprints are compared to an Efficient Neural Architecture Search as a well-known AutoML approach. We show the potential of Green-AutoML by outperforming the original plastic detection system by 1.1% in accuracy and using 33 times fewer floating point operations at inference, and only 29% of the carbon emissions of the best-known baseline. This shows the large potential of AutoML on climate-change relevant applications and at the same time contributes to more efficient modern Deep Learning systems, saving substantial resources and reducing the carbon footprint. Authors: Daphne Theodorakopoulos (German Research Centre for Artificial Intelligence (DFKI), Marine Perception Department and Leibniz University Hannover, Institute of Artificial Intelligence); Christoph Manß (German Research Centre for Artificial Intelligence (DFKI), Marine Perception Department); Frederic Stahl (German Research Centre for Artificial Intelligence (DFKI), Marine Perception Department); Marius Lindauer (Leibniz University Hannover)	Oceans & Marine Systems Computer Vision & Remote Sensing
ICLR 2023	Robustly modeling the nonlinear impact of climate change on agriculture by combining econometrics and machine learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate change is expected to have a dramatic impact on agricultural production; however, due to natural complexity, the exact avenues and relative strengths by which this will happen are still unknown. The development of accurate forecasting models is thus of great importance to enable policy makers to design effective interventions. To date, most machine learning methods aimed at tackling this problem lack a consideration of causal structure, thereby making them unreliable for the types of counterfactual analysis necessary when making policy decisions. Econometrics has developed robust techniques for estimating cause-effect relations in time-series, specifically through the use of cointegration analysis and Granger causality. However, these methods are frequently limited in flexibility, especially in the estimation of nonlinear relationships. In this work, we propose to integrate the non-linear function approximators with the robust causal estimation methods to ultimately develop an accurate agricultural forecasting model capable of robust counterfactual analysis. This method would be a valuable new asset for government and industrial stakeholders to understand how climate change impacts agricultural production. Authors: Benedetta Francesconi (Independent Researcher); Ying-Jung C Deweese (Descartes Labs / Georgia Insititute of Technology)	Agriculture & Food Climate Science & Modeling Public Policy Societal Adaptation & Resilience Time-series Analysis
ICLR 2023	Towards Green, Accurate, and Efficient AI Models Through Multi-Objective Optimization (Proposals Track) Abstract and authors: (click to expand) Abstract: Machine learning is one of the fastest growing services in modern hyperscale data centers. While AI’s exponential scaling has enabled unprecedented modeling capabilities across computer vision, natural language processing, protein modeling, personalized recommendation, it comes at the expense of significant energy and environmental footprints. This work aims to co-optimize machine learning models in terms of their accuracy, compute efficiency, and environmental sustainability by using multi-objective bayesian optimization. We aim to extend current multi-objective optimization frameworks, such as the openly available Ax (adaptive experimentation) platform to balance accuracy, efficiency, and environmental sustainability of deep neural networks. In order to optimize for environmental sustainability we will consider the impact across AI model life cycles (e.g., training, inference) and hardware life cycles (e.g., manufacturing, operational use). Given this is a research proposal, we expect to demonstrate that designing for sustainable AI models yields distinct optimal neural network architectures than ones designed for accuracy and efficiency given the external impacts of varying renewable energy and tradeoffs between compute and storage for embodied carbon overheads. Authors: Udit Gupta (Harvard University); Daniel R Jiang (Meta); Maximilian Balandat (Facebook); Carole-Jean Wu (Meta AI)
ICLR 2023	EfficientTempNet: Temporal Super-Resolution of Radar Rainfall (Papers Track) Abstract and authors: (click to expand) Abstract: Rainfall data collected by various remote sensing instruments such as radars or satellites has different space-time resolutions. This study aims to improve the temporal resolution of radar rainfall products to help with more accurate climate change modeling and studies. In this direction, we introduce a solution based on EfficientNetV2, namely EfficientTempNet, to increase the temporal resolution of radar-based rainfall products from 10 minutes to 5 minutes. We tested EfficientRainNet over a dataset for the state of Iowa, US, and compared its performance to three different baselines to show that EfficientTempNet presents a viable option for better climate change monitoring. Authors: Bekir Z Demiray (University of Iowa); Muhammed A Sit (The University of Iowa); Ibrahim Demir (University of Iowa)	Earth Observation & Monitoring Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Time-series Analysis
ICLR 2023	Bird Distribution Modelling using Remote Sensing and Citizen Science data (Papers Track) Overall Best Paper Abstract and authors: (click to expand) Abstract: Climate change is a major driver of biodiversity loss, changing the geographic range and abundance of many species. However, there remain significant knowl- edge gaps about the distribution of species, due principally to the amount of effort and expertise required for traditional field monitoring. We propose an approach leveraging computer vision to improve species distribution modelling, combining the wide availability of remote sensing data with sparse on-ground citizen science data from .We introduce a novel task and dataset for mapping US bird species to their habitats by predicting species encounter rates from satellite images, along with baseline models which demonstrate the power of our approach. Our methods open up possibilities for scalably modelling ecosystems properties worldwide. Authors: Mélisande Teng (Mila, Université de Montréal); Amna Elmustafa (African Institute for Mathematical Science); Benjamin Akera (McGill University); Hugo Larochelle (UdeS); David Rolnick (McGill University, Mila)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
ICLR 2023	Efficient HVAC Control with Deep Reinforcement Learning and EnergyPlus (Papers Track) Abstract and authors: (click to expand) Abstract: Heating and cooling comprise a significant fraction of the energy consumed by buildings, which in turn account for a significant fraction of society’s energy use. Most building heating, ventilation, and air conditioning (HVAC) systems use standard control schemes that meet basic operating constraints and comfort requirements but with suboptimal efficiency. Deep reinforcement learning (DRL) has shown immense potential for high-performing control in a variety of simulated settings, but has not been widely deployed for real-world control. Here we provide two contributions toward increasing the viability of real-world, DRL-based HVAC control, leveraging the EnergyPlus building simulator. First, we use the new EnergyPlus Python API to implement a first-of-its-kind, purely Python-based EnergyPlus DRL learning framework capable of generalizing to a wide variety of building configurations and weather scenarios. Second, we demonstrate an approach to constrained learning for this setting, removing the requirement to tune reward functions in order to maximize energy efficiency given temperature constraints. We tested our framework on realistic building models of a data center, an office building, and a secondary school. In each case, trained agents maintained temperature control while achieving energy savings relative to standard approaches. Authors: Jared Markowitz (Johns Hopkins University Applied Physics Laboratory); Nathan Drenkow (Johns Hopkins University Applied Physics Laboratory)	Buildings Reinforcement Learning
ICLR 2023	Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling (Papers Track) Abstract and authors: (click to expand) Abstract: Running climate simulations informs us of future climate change. However, it is computationally expensive to resolve complex climate processes numerically. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations. So far, all neural network downscaling models can only downscale input samples with a pre-defined upsampling factor. In this work, we propose a Fourier neural operator downscaling model. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high-resolutions. Evaluated on Navier-Stokes equation solution data and ERA5 water content data, our downscaling model demonstrates better performance than widely used convolutional and adversarial generative super-resolution models in both learned and zero-shot downscaling. Our model's performance is further boosted when a constraint layer is applied. In the end, we show that by combining our downscaling model with a low-resolution numerical PDE solver, the downscaled solution outperforms the solution of the state-of-the-art high-resolution data-driven solver. Our model can be used to cheaply and accurately generate arbitrarily high-resolution climate simulation data with fast-running low-resolution simulation as input. Authors: Qidong Yang (New York University); Paula Harder (Fraunhofer ITWM); Venkatesh Ramesh (University of Montreal, Mila); Alex Hernandez-Garcia (Mila - Quebec AI Institute); Daniela Szwarcman (IBM Research); Prasanna Sattigeri (IBM Research); Campbell D Watson (IBM Reserch); David Rolnick (McGill University, Mila)	Climate Science & Modeling Earth Observation & Monitoring Computer Vision & Remote Sensing Generative Modeling
ICLR 2023	Data-driven multiscale modeling of subgrid parameterizations in climate models (Papers Track) Best ML Innovation Abstract and authors: (click to expand) Abstract: Subgrid parameterizations that represent physical processes occurring below the resolution of current climate models are an important component in producing accurate, long-term predictions for the climate. A variety of approaches have been tested to design these components, including deep learning methods. In this work, we evaluate a proof of concept illustrating a multiscale approach to this prediction problem. We train neural networks to predict subgrid forcing values on a testbed model and examine improvements in prediction accuracy which can be obtained by using additional information in both fine-to-coarse and coarse-to-fine directions. Authors: Karl Otness (New York University); Laure Zanna (NYU); Joan Bruna (NYU)	Climate Science & Modeling Hybrid Physical Models
ICLR 2023	Decision-aware uncertainty-calibrated deep learning for robust energy system operation (Proposals Track) Abstract and authors: (click to expand) Abstract: Decision-making under uncertainty is an important problem that arises in many domains. Achieving robustness guarantees requires well-calibrated uncertainties, which can be difficult to achieve in high-capacity prediction models such as deep neural networks. This paper proposes an end-to-end approach for learning uncertainty-calibrated deep learning models that directly optimizes a downstream decision-making objective with provable robustness. We also propose two concrete applications in energy system operations, including a grid scheduling task as well as an energy storage arbitrage task. As renewable wind and solar generation increasingly proliferate and their variability penetrates the energy grid, learning uncertainty-aware predictive models becomes increasingly crucial for maintaining efficient and reliable grid operation. Authors: Christopher Yeh (California Institute of Technology); Nicolas Christianson (California Institute of Technology); Steven Low (California Institute of Technology); Adam Wierman (California Institute of Technology); Yisong Yue (Caltech)	Power & Energy Reinforcement Learning
ICLR 2023	Multi-Agent Deep Reinforcement Learning for Solar-Battery System to Mitigate Solar Curtailment in Real-Time Electricity Market (Papers Track) Abstract and authors: (click to expand) Abstract: The increased uptake of solar energy in the energy transition towards decarbonization has caused the issue of solar photovoltaic (PV) curtailments, resulting in significant economic losses and hindering the energy transition. To overcome this issue, battery energy storage systems (BESS) can serve as onsite backup sources for solar farms. However, the backup role of the BESS significantly limits its economic value, disincentivizing the BESS deployment due to high investment costs. Hence, it is essential to effectively reduce solar curtailment while ensuring viable operations of the BESS. Authors: Jinhao Li (Monash University); Changlong Wang (Monash University); Hao Wang (Monash University)	Power & Energy Reinforcement Learning
ICLR 2023	Projecting the climate penalty on pm2.5 pollution with spatial deep learning (Proposals Track) Abstract and authors: (click to expand) Abstract: The climate penalty measures the effects of a changing climate on air quality due to the interaction of pollution with climate factors, independently of future changes in emissions. This work introduces a statistical framework for estimating the climate penalty on soot pollution (PM 2.5), which has been linked to respiratory and cardiovascular diseases and premature mortality. The framework evaluates the disparities in future PM 2.5 exposure across racial/ethnic and income groups---an important step towards informing mitigation public health policy and promoting environmental equity in addressing the effects of climate change. The proposed methodology aims to improve existing statistical-based methods for estimating the climate penalty using an expressive and scalable predictive model based on spatial deep learning with spatiotemporal trend estimation. The proposed approach will (1) use higher-resolution climate inputs, which current statistical methods to estimate the climate penalty approaches cannot accommodate; (2) integrate additional predictive data sources such as demographics, geology, and land use; (3) consider regional dependencies and synoptic weather patterns influencing PM 2.5, deconvolving the effects of climate change from increasing air quality regulations and other sources of unmeasured spatial heterogeneity. Authors: Mauricio Tec (Harvard University); Riccardo Cadei (Harvard University); Francesca Dominici (Harvard University); Corwin Zigler (University of Texas at Austin)	Climate Justice Health Computer Vision & Remote Sensing Time-series Analysis
ICLR 2023	On the impact of small-data diversity on forecasts: evidence from meteorologically-driven electricity demand in Mediterranean zones. (Papers Track) Abstract and authors: (click to expand) Abstract: In this paper, we compare the improvement of probabilistic electricity demand forecasts for three specific coastal and island regions using raw and pre-computed meteorological features based on empirically-tested formulations drawn from climate science literature. Typically for the general task of time-series forecasting with strong weather/climate drivers, go-to models like the Autoregressive Integrated Moving Average (ARIMA) model are built with assumptions of how independent variables will affect a dependent one and are at best encoded with a handful of exogenous features with known impact. Depending on the geographical region and/or cultural practices of a population, such a selection process may yield a non-optimal feature set which would ultimately drive a weak impact on underline demand forecasts. The aim of this work is to assess the impact of a documented set of meteorological features on electricity demand using deep learning models in comparative studies. Leveraging the defining computational architecture of the Temporal Fusion Transformer (TFT), we discover the unimportance of weather features for improving probabilistic forecasts for the targeted regions. However, through experimentation, we discover that the more stable electricity demand of the coastal Mediterranean regions, the Ceuta and Melilla autonomous cities in Morocco, improved the forecast accuracy of the strongly tourist-driven electricity demand for the Balearic islands located in Spain during the time of travel restrictions (i.e., during COVID19 (2020))--a root mean squared error (RMSE) from ~0.090 to ~0.012 with a substantially improved 10th/90th quantile bounding. Authors: Reginald Bryant (IBM Research - Africa); Julian Kuehnert (IBM Research)	Time-series Analysis Cities & Urban Planning Climate Science & Modeling Extreme Weather Societal Adaptation & Resilience Uncertainty Quantification & Robustness
ICLR 2023	Artificial Intelligence in Tropical Cyclone Forecasting (Proposals Track) Abstract and authors: (click to expand) Abstract: Tropical cyclones (TC) in Bangladesh and other developing nations harm property and human lives. Theoretically, artificial intelligence (AI) can anticipate TC using tracking, intensity, and cyclone aftereffect phenomena. Although AI has a significant impact on predicting, poorer nations have struggled to adjust to its real-world applications. The interpretability of such a solution from an AI-based solution is the main factor in that situation, together with the infrastructure. Explainable AI has been extensively employed in the medical field because the outcome is so important. We believe that using explainable AI in TC forecasting is equally important as one large forecast can cause the thought of life loss. Additionally, it will improve the long-term viability of the AI-based weather forecasting system. To be more specific, we want to employ explainable AI in every way feasible in this study to address the problems of TC forecasting, intensity estimate, and tracking. We'll do this by using the graph neural network. The adoption of AI-based solutions in underdeveloped nations will be aided by this solution, which will boost their acceptance. With this effort, we also hope to tackle the challenge of sustainable AI in order to tackle the issue of climate change on a larger scale. However, Cyclone forecasting might be transformed by sustainable AI, guaranteeing precise and early predictions to lessen the effects of these deadly storms. The examination of vast volumes of meteorological data to increase forecasting accuracy is made possible by the combination of AI algorithms and cutting-edge technologies like machine learning and big data analytics. Improved accuracy is one of the main advantages of sustainable AI for cyclone prediction. To provide more precise forecasts, AI systems can evaluate a lot of meteorological data, including satellite imagery and ocean temperature readings. Authors: Dr. Nusrat Sharmin (Military Institute of Science and Technology); Professor Dr. Md. Mahbubur Rahman Rahman (Military Institute of Science and Technology (MIST)); Sabbir Rahman (Military Institute of Science and Technology); Mokhlesur Rahman (Military Institute of Science and Technology)	Climate Science & Modeling Disaster Management and Relief Interpretable ML
NeurIPS 2022	Function Approximations for Reinforcement Learning Controller for Wave Energy Converters (Papers Track) Abstract and authors: (click to expand) Abstract: Waves are a more consistent form of clean energy than wind and solar and the latest Wave Energy Converters (WEC) platforms like CETO 6 have evolved into complex multi-generator designs with a high energy capture potential for financial viability. Multi-Agent Reinforcement Learning (MARL) controller can handle these complexities and control the WEC optimally unlike the default engineering controllers like Spring Damper which suffer from lower energy capture and mechanical stress from the spinning yaw motion. In this paper, we look beyond the normal hyper-parameter and MARL agent tuning and explored the most suitable architecture for the neural network function approximators for the policy and critic networks of MARL which act as its brain. We found that unlike the commonly used fully connected network (FCN) for MARL, the sequential models like transformers and LSTMs can model the WEC system dynamics better. Our novel transformer architecture, Skip Transformer-XL (STrXL), with several gated residual connections in and around the transformer block performed better than the state-of-the-art with faster training convergence. STrXL boosts energy efficiency by an average of 25% to 28% over the existing spring damper (SD) controller for waves at different angles and almost eliminated the mechanical stress from the rotational yaw motion, saving costly maintenance on open seas, and thus reducing the Levelized Cost of wave energy (LCOE). Demo: https://tinyurl.com/4s4mmb9v Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Alexander Shmakov (UC Irvine); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Alexandre Pichard (Carnegie Clean Energy); Mathieu Cocho (Carnegie Clean Energy)	Power & Energy Reinforcement Learning
NeurIPS 2022	Image-Based Soil Organic Carbon Estimation from Multispectral Satellite Images with Fourier Neural Operator and Structural Similarity (Papers Track) Abstract and authors: (click to expand) Abstract: Soil organic carbon (SOC) sequestration is the transfer and storage of atmospheric carbon dioxide in soils, which plays an important role in climate change mitigation. SOC concentration can be improved by proper land use, thus it is beneficial if SOC can be estimated at a regional or global scale. As multispectral satellite data can provide SOC-related information such as vegetation and soil properties at a global scale, estimation of SOC through satellite data has been explored as an alternative to manual soil sampling. Although existing works show promising results, most studies are based on pixel-based approaches with traditional machine learning methods, and convolutional neural networks (CNNs) are seldom used. To study the advantages of using CNNs on SOC remote sensing, in this paper, we propose the FNO-DenseNet based on the state-of-the-art Fourier neural operator (FNO). By combining the advantages of the FNO and DenseNet, the FNO-DenseNet outperformed the FNO in our experiments with hundreds of times fewer parameters. The FNO-DenseNet also outperformed a pixel-based random forest by 18% in the mean absolute percentage error. To the best of our knowledge, this is the first work of applying the FNO on SOC remote sensing. Authors: Ken C. L. Wong (IBM Research – Almaden Research Center); Levente Klein (IBM Research); Ademir Ferreira da Silva (IBM Research); Hongzhi Wang (IBM Almaden Research Center); Jitendra Singh (IBM Research - India); Tanveer Syeda-Mahmood (IBM Research)	Carbon Capture & Sequestration Computer Vision & Remote Sensing
NeurIPS 2022	SolarDK: A high-resolution urban solar panel image classification and localization dataset (Papers Track) Abstract and authors: (click to expand) Abstract: The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/. Authors: Maxim MK Khomiakov (DTU); Julius Radzikowski (DTU); Carl Schmidt (DTU); Mathias Sørensen (DTU); Mads Andersen (DTU); Michael Andersen (Technical University of Denmark); Jes Frellsen (Technical University of Denmark)	Buildings Power & Energy Computer Vision & Remote Sensing
NeurIPS 2022	Bayesian inference for aerosol vertical profiles (Papers Track) Abstract and authors: (click to expand) Abstract: Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of the anthropogenic climate change. This uncertainty arises in part from the difficulty in measuring the vertical distributions of aerosols. We often have to settle for less informative vertically aggregated proxies such as aerosol optical depth (AOD). In this work, we develop a framework to infer vertical aerosol profiles using AOD and readily available vertically resolved meteorological predictors such as temperature or relative humidity. We devise a simple Gaussian process prior over aerosol vertical profiles and update it with AOD observations. We validate our approach using ECHAM-HAM aerosol-climate model data. Our results show that, while simple, our model is able to reconstruct realistic extinction profiles with well-calibrated uncertainty. In particular, the model demonstrates a faithful reconstruction of extinction patterns arising from aerosol water uptake in the boundary layer. Authors: Shahine Bouabid (University of Oxford); Duncan Watson-Parris (University of Oxford); Dino Sejdinovic (University of Adelaide)	Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2022	Optimizing toward efficiency for SAR image ship detection (Papers Track) Abstract and authors: (click to expand) Abstract: The detection and prevention of illegal fishing is critical to maintaining a healthy and functional ecosystem. Recent research on ship detection in satellite imagery has focused exclusively on performance improvements, disregarding detection efficiency. However, the speed and compute cost of vessel detection are essential for a timely intervention to prevent illegal fishing. Therefore, we investigated optimization methods that lower detection time and cost with minimal performance loss. We trained an object detection model based on a convolutional neural network (CNN) using a dataset of satellite images. Then, we designed two efficiency optimizations that can be applied to the base CNN or any other base model. The optimizations consist of a fast, cheap classification model and a statistical algorithm. The integration of the optimizations with the object detection model leads to a trade-off between speed and performance. We studied the trade-off using metrics that give different weight to execution time and performance. We show that by using a classification model the average precision of the detection model can be approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time. Authors: Arthur Van Meerbeeck (KULeuven); Ruben Cartuyvels (KULeuven); Jordy Van Landeghem (KULeuven); Sien Moens (KU Leuven)	Computer Vision & Remote Sensing Ecosystems & Biodiversity Oceans & Marine Systems
NeurIPS 2022	AutoML-based Almond Yield Prediction and Projection in California (Papers Track) Abstract and authors: (click to expand) Abstract: Almonds are one of the most lucrative products of California, but are also among the most sensitive to climate change. In order to better understand the relationship between climatic factors and almond yield, an automated machine learning framework is used to build a collection of machine learning models. The prediction skill is assessed using historical records. Future projections are derived using 17 downscaled climate outputs. The ensemble mean projection displays almond yield changes under two different climate scenarios, along with two technology development scenarios, where the role of technology development is highlighted. The mean projections and distributions provide insightful results to stakeholders and can be utilized by policymakers for climate adaptation. Authors: Shiheng Duan (Lawrence Livermore National Laboratory); Shuaiqi Wu (University of California, Davis); Erwan Monier (University of California, Davis); Paul Ullrich (University of California, Davis)	Agriculture & Food
NeurIPS 2022	Attention-Based Scattering Network for Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties. Combining these features in an effective manner to create a performant and trustworthy model is of utmost importance to forecasters. Neural networks show promise, yet suffer from unintuitive computations, fusion of high-level features, and may be limited by the quantity of available data. In this work, we leverage the scattering transform to extract high-level features without additional trainable parameters and introduce a separation scheme to bring attention to independent input channels. Experiments show promising results on estimating tropical cyclone intensity and predicting the occurrence of lightning from satellite imagery. Authors: Jason Stock (Colorado State University); Charles Anderson (Colorado State University)	Climate Science & Modeling Earth Observation & Monitoring Interpretable ML
NeurIPS 2022	Discovering Interpretable Structural Model Errors in Climate Models (Papers Track) Abstract and authors: (click to expand) Abstract: Inaccuracies in the models of the Earth system, i.e., structural and parametric model errors, lead to inaccurate climate change projections. Errors in the model can originate from unresolved phenomena due to a low numerical resolution, as well as misrepresentations of physical phenomena or boundaries (e.g., orography). Therefore, such models lead to inaccurate short--term forecasts of weather and extreme events, and more importantly, long term climate projections. While calibration methods have been introduced to address for parametric uncertainties, e.g., by better estimation of system parameters from observations, addressing structural uncertainties, especially in an interpretable manner, remains a major challenge. Therefore, with increases in both the amount and frequency of observations of the Earth system, algorithmic innovations are required to identify interpretable representations of the model errors from observations. We introduce a flexible, general-purpose framework to discover interpretable model errors, and show its performance on a canonical prototype of geophysical turbulence, the two--level quasi--geostrophic system. Accordingly, a Bayesian sparsity--promoting regression framework is proposed, that uses a library of kernels for discovery of model errors. As calculating the library from noisy and sparse data (e.g., from observations) using convectional techniques leads to interpolation errors, here we use a coordinate-based multi--layer embedding to impute the sparse observations. We demonstrate the importance of alleviating spectral bias, and propose a random Fourier feature layer to reduce it in the proposed embeddings, and subsequently enable an accurate discovery. Our framework is demonstrated to successfully identify structural model errors due to linear and nonlinear processes (e.g., radiation, surface friction, advection), as well as misrepresented orography. Authors: Rambod Mojgani (Rice University); Ashesh K Chattopadhyay (Rice University); Pedram Hassanzadeh (Rice University)	Climate Science & Modeling Earth Observation & Monitoring Interpretable ML
NeurIPS 2022	Aboveground carbon biomass estimate with Physics-informed deep network (Papers Track) Abstract and authors: (click to expand) Abstract: The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of key state parameters including the aboveground carbon biomass (AGB) is required. We use deep neural network to generate a wall-to-wall map of AGB within the Continental USA (CONUS) with 30-meter spatial resolution for the year 2021. We combine radar and optical hyperspectral imagery, with a physical climate parameter of solar-induced chlorophyll fluorescence (SIF)-based gross primary productivity (GPP). Validation results show that a masked variation of UNet has the lowest validation RMSE of 37.93 ± 1.36 Mg C/ha, as compared to 52.30 ± 0.03 Mg C/ha for random forest algorithm. Furthermore, models that learn from SIF-based GPP in addition to radar and optical imagery reduce validation RMSE by almost 10% and the standard deviation by 40%. Finally, we apply our model to measure losses in AGB from the recent 2021 Caldor wildfire in California, and validate our analysis with Sentinel-based burn index. Authors: Juan Nathaniel (Columbia University); Levente Klein (IBM Research); Campbell D Watson (IBM Reserch); Gabrielle Nyirjesy (Columbia University); Conrad M Albrecht (IBM Research)	Computer Vision & Remote Sensing Forests Hybrid Physical Models
NeurIPS 2022	Improving the predictions of ML-corrected climate models with novelty detection (Papers Track) Abstract and authors: (click to expand) Abstract: While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. Because ML-predicted corrections feed back into the climate model’s base physics, the ML-corrected model regularly produces out of sample data, which can cause model instability and frequent crashes. This work shows that adding semi-supervised novelty detection to identify out-of-sample data and disable the ML-correction accordingly stabilizes simulations and sharply improves the quality of predictions. We design an augmented climate model with a one-class support vector machine (OCSVM) novelty detector that provides better temperature and precipitation forecasts in a year-long simulation than either a baseline (no-ML) or a standard ML-corrected run. By improving the accuracy of coarse-grid climate models, this work helps make accurate climate models accessible to researchers without massive computational resources. Authors: Clayton H Sanford (Columbia); Anna Kwa (Allen Institute for Artificial Intelligence); Oliver Watt-Meyer (Allen Institute for AI); Spencer Clark (Allen Institute for AI); Noah Brenowitz (Allen Institute for AI); Jeremy McGibbon (Allen Institute for AI); Christopher Bretherton (Allen Institute for AI)	Climate Science & Modeling Hybrid Physical Models Uncertainty Quantification & Robustness Unsupervised & Semi-Supervised Learning
NeurIPS 2022	Levee protected area detection for improved flood risk assessment in global hydrology models (Papers Track) Abstract and authors: (click to expand) Abstract: Precise flood risk assessment is needed to reduce human societies vulnerability as climate change increases hazard risk and exposure related to floods. Levees are built to protect people and goods from flood, which alters river hydrology, but are still not accounted for by global hydrological model. Detecting and integrating levee structures to global hydrological simulations is thus expected to enable more precise flood simulation and risk assessment, with important consequences for flood risk mitigation. In this work, we propose a new formulation to the problem of identifying levee structures: instead of detecting levees themselves, we focus on segmenting the region of the floodplain they protect. This formulation allows to better identify protected areas, to leverage the structure of hydrological data, and to simplify the integration of levee information to global hydrological models. Authors: Masato Ikegawa (Kobe University); Tristan E.M Hascoet (Kobe University); Victor Pellet (Observatoire de Paris); Xudong Zhou (The University of Tokyo); Tetsuya Takiguchi (Kobe University); Dai Yamazaki (The University of Tokyo)	Disaster Management and Relief
NeurIPS 2022	Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification (Papers Track) Abstract and authors: (click to expand) Abstract: Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia. Authors: Joseph Early (University of Southampton); Ying-Jung C Deweese (Georgia Insititute of Technology); Christine Evers (University of Southampton); Sarvapali Ramchurn (University of Southampton)	Land Use Earth Observation & Monitoring Agriculture & Food Cities & Urban Planning Forests Interpretable ML Unsupervised & Semi-Supervised Learning
NeurIPS 2022	Deep learning for downscaling tropical cyclone rainfall (Papers Track) Abstract and authors: (click to expand) Abstract: Flooding is often the leading cause of mortality and damages from tropical cyclones. With rainfall from tropical cyclones set to rise under global warming, better estimates of extreme rainfall are required to better support resilience efforts. While high resolution climate models capture tropical cyclone statistics well, they are computationally expensive leading to a trade-off between accuracy and generating enough ensemble members to generate sufficient high impact, low probability events. Often, downscaling models are used as a computationally cheaper alternative. Here, we develop and evaluate a set of deep learning models for downscaling tropical cyclone rainfall for more robust risk analysis. Authors: Emily Vosper (University of Bristol); Lucy Harris (University of Oxford); Andrew McRae (University of Oxford); Laurence Aitchison (University of Bristol); Peter Watson (Bristol); Raul Santos Rodriguez (University of Bristol); Dann Mitchell (University of Bristol)	Extreme Weather Climate Science & Modeling Interpretable ML
NeurIPS 2022	Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes (Papers Track) Abstract and authors: (click to expand) Abstract: Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management. Ideally these forecasts are equipped with error bars, so that downstream decisions can account for uncertainty. To produce predictions with error bars in this setting, we consider Gaussian processes (GPs) for modelling and predicting solar photovoltaic energy production in the UK. A standard application of GP regression on the PV timeseries data is infeasible due to the large data size and non-Gaussianity of PV readings. However, this is made possible by leveraging recent advances in scalable GP inference, in particular, by using the state-space form of GPs, combined with modern variational inference techniques. The resulting model is not only scalable to large datasets but can also handle continuous data streams via Kalman filtering. Authors: So Takao (UCL); Sean Nassimiha (UCL); Peter Dudfield (Open Climate Fix); Jack Kelly (Open Climate Fix); Marc Deisenroth (University College London)	Time-series Analysis Climate Science & Modeling Power & Energy Causal & Bayesian Methods Uncertainty Quantification & Robustness
NeurIPS 2022	Identifying latent climate signals using sparse hierarchical Gaussian processes (Papers Track) Abstract and authors: (click to expand) Abstract: Extracting latent climate signals from multiple climate model simulations is important to estimate future climate change. To tackle this we develop a sparse hierarchical Gaussian process (SHGP), which probabilistically learns a latent distribution from a set of vectors. We use this to predict the latent surface temperature change globally and for central England from an ensemble of climate models, in a scalable manner and with robust uncertainty propagation. Authors: Matt Amos (Lancaster University); Thomas Pinder (Lancaster University); Paul Young (Lancaster University)	Climate Science & Modeling Causal & Bayesian Methods Uncertainty Quantification & Robustness
NeurIPS 2022	Towards dynamical stability analysis of sustainable power grids using Graph Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: To mitigate climate change, the share of renewable needs to be increased. Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and volatility in production. The operation of sustainable power grids with a high penetration of renewable energies requires new methods to analyze the dynamical stability. We provide new datasets of dynamical stability of synthetic power grids, and find that graph neural networks (GNNs) are surprisingly effective at predicting the highly non-linear target from topological information only. To illustrate the potential to scale to real-sized power grids, we demonstrate the successful prediction on a Texan power grid model. Authors: Christian Nauck (PIK); Michael Lindner (PIK); Konstantin Schürholt (University of St. Gallen); Frank Hellmann (PIK)	Power & Energy
NeurIPS 2022	Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation (Papers Track) Abstract and authors: (click to expand) Abstract: The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (∼30m). We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning technique able to automatically detect plumes over large areas. To compensate for the sparse database of PRISMA images, we trained our model by transposing high resolution plumes from Sentinel-2 to PRISMA. Our methodology avoids computationally expensive synthetic plume from Large Eddy Simulations while generating a broad and realistic training database, and paves the way for large-scale detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT, CarbonMapper). Authors: Alexis Groshenry (Kayrros); Clément Giron (Kayrros); Alexandre d'Aspremont (CNRS, DI, Ecole Normale Supérieure; Kayrros); Thomas Lauvaux (University of Reims Champagne Ardenne, GSMA, UMR 7331); Thibaud Ehret (Centre Borelli)	Computer Vision & Remote Sensing Earth Observation & Monitoring Climate Science & Modeling Meta- and Transfer Learning
NeurIPS 2022	Probabilistic forecasting of regional photovoltaic power production based on satellite-derived cloud motion (Papers Track) Abstract and authors: (click to expand) Abstract: Solar energy generation drastically increased in the last years, and it is expected to grow even more in the next decades. So, accurate intra-day forecasts are needed to improve the predictability of the photovoltaic power production and associated balancing measures to increase the shares of renewable energy in the power grid. Most forecasting methods require numerical weather predictions, which are slow to compute, or long-term datasets to run the forecast. These issues make the models difficult to implement in an operational setting. To overcome these problems, we propose a novel regional intraday probabilistic PV power forecasting model able to exploit only 2 hours of satellite-derived cloudiness maps to produce the ensemble forecast. The model is easy to implement in an operational setting as it is based on Pysteps, an already-operational Python library for precipitation nowcasting. With few adaptations of the Steps algorithm, we reached state-of-the-art performance, reaching a 71% lower RMSE than the Persistence model and a 50% lower CRPS than the Persistence Ensemble model for forecast lead times up to 4 hours. Authors: Alberto Carpentieri (Bern University of Applied Science); Doris Folini (Institute for Atmospheric and Climate Science, ETH Zurich); Martin Wild (Institute for Atmospheric and Climate Science, ETH Zurich); Angela Meyer (Bern University of Applied Science)	Power & Energy Computer Vision & Remote Sensing Uncertainty Quantification & Robustness
NeurIPS 2022	Robustifying machine-learned algorithms for efficient grid operation (Papers Track) Abstract and authors: (click to expand) Abstract: We propose a learning-augmented algorithm, RobustML, for operation of dispatchable generation that exploits the good performance of a machine-learned algorithm while providing worst-case guarantees on cost. We evaluate the algorithm on a realistic two-generator system, where it exhibits robustness to distribution shift while enabling improved efficiency as renewable penetration increases. Authors: Nicolas Christianson (California Institute of Technology); Christopher Yeh (California Institute of Technology); Tongxin Li (The Chinese University of Hong Kong (Shenzhen)); Mahdi Torabi Rad (Beyond Limits); Azarang Golmohammadi (Beyond Limits, Inc.); Adam Wierman (California Institute of Technology)	Uncertainty Quantification & Robustness Power & Energy Reinforcement Learning
NeurIPS 2022	Deep Hydrology: Hourly, Gap-Free Flood Maps Through Joint Satellite and Hydrologic Modelling (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change-driven weather disasters are rapidly increasing in both frequency and magnitude. Floods are the most damaging of these disasters, with approximately 1.46 billion people exposed to inundation depths of over 0.15m, a significant life and livelihood risk. Accurate knowledge of flood-extent for ongoing and historical events facilitates climate adaptation in flood-prone communities by enabling near real-time disaster monitoring to support planning, response, and relief during these extreme events. Satellite observations can be used to derive flood-extent maps directly; however, these observations are impeded by cloud and canopy cover, and can be very infrequent and hence miss the flood completely. In contrast, physically-based inundation models can produce spatially complete event maps but suffer from high uncertainty if not frequently calibrated with expensive land and infrastructure surveys. In this study, we propose a deep learning approach to reproduce satellite-observed fractional flood-extent maps given dynamic state variables from hydrologic models, fusing information contained within the states with direct observations from satellites. Our model has an hourly temporal resolution, contains no cloud-gaps, and generalizes to watersheds across the continental United States with a 6% error on held-out areas that never flooded before. We further demonstrate through a case study in Houston, Texas that our model can distinguish tropical cyclones that caused flooding from those that did not within two days of landfall, thereby providing a reliable source for flood-extent maps that can be used by disaster monitoring services. Authors: Tanya Nair (Cloud To Street); Veda Sunkara (Cloud to Street); Jonathan Frame (Cloud to Street); Philip Popien (Cloud to Street); Subit Chakrabarti (Cloud To Street)	Computer Vision & Remote Sensing Climate Justice Climate Science & Modeling Disaster Management and Relief Extreme Weather Hybrid Physical Models
NeurIPS 2022	Machine learning emulation of a local-scale UK climate model (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic high-resolution rainfall predictions based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation. Authors: Henry Addison (University of Bristol); Elizabeth Kendon (Met Office Hadley Centre); Suman Ravuri (DeepMind); Laurence Aitchison (University of Bristol); Peter Watson (Bristol)	Climate Science & Modeling Extreme Weather Generative Modeling
NeurIPS 2022	Bridging the Microwave Data Gap; Using Bayesian Deep Learning to “See” the Unseen (Papers Track) Abstract and authors: (click to expand) Abstract: Having microwave data with the spatial and temporal resolution of infrared data would provide a large positive impact on many climate and weather applications. We demonstrate that Bayesian deep learning is a promising technique for both creating and improving synthetic microwave data from infrared data. We report 0.7% mean absolute percentage error for 183+/-3 GHz microwave brightness temperature and uncertainty metrics and find that more training data is needed to achieve improved performance at 166 GHz, 37 GHz, and 23 GHz. Analysis of the spatial distribution of uncertainty reveals that additional cloud data will provide the greatest increase in skill, which will potentially allow for generation of many secondary products derived from microwave data in the future. Authors: Pedro Ortiz (Naval Postgraduate School); Eleanor Casas (Naval Postgraduate School); Marko Orescanin (Naval Postgraduate School); Scott Powell (Naval Postgraduate School)	Earth Observation & Monitoring Climate Science & Modeling Extreme Weather Causal & Bayesian Methods Uncertainty Quantification & Robustness
NeurIPS 2022	Learning evapotranspiration dataset corrections from water cycle closure supervision (Papers Track) Abstract and authors: (click to expand) Abstract: Evapotranspiration (ET) is one of the most uncertain components of the global water cycle. Improving global ET estimates is needed to better our understanding of the global water cycle so as to forecast the consequences of climate change on the future of global water resource distribution. This work presents a methodology to derive monthly corrections of global ET datasets at 0.25 degree resolution. We use ML to generalize sparse catchment-level water cycle closure residual information to global and dense pixel-level residuals. Our model takes a probabilistic view on ET datasets and their correction that we use to regress catchment-level residuals using a sum-aggregated supervision. Using four global ET datasets, we show that our learned model has learned ET corrections that accurately generalize its water cycle-closure results to unseen catchments. Authors: Tristan E.M Hascoet (Kobe University); Victor Pellet (LERMA); Filipe Aires (LERMA)	Earth Observation & Monitoring Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2022	Convolutional Neural Processes for Inpainting Satellite Images: Application to Water Body Segmentation (Papers Track) Abstract and authors: (click to expand) Abstract: The widespread availability of satellite images has allowed researchers to monitor the impact of climate on socio-economic and environmental issues through examples like crop and water body classification to measure food scarcity and risk of flooding. However, a common issue of satellite images is missing values due to measurement defects, which render them unusable by existing methods without data imputation. To repair the data, inpainting methods can be employed, which are based on classical PDEs or interpolation methods. Recently, deep learning approaches have shown promise in this realm, however many of these methods do not explicitly take into account the inherent spatio-temporal structure of satellite images. In this work, we cast satellite image inpainting as a meta-learning problem, and implement Convolutional Neural Processes (ConvNPs) in which we frame each satellite image as its own task or 2D regression problem. We show that ConvNPs outperform classical methods and state-of-the-art deep learning inpainting models on a scanline problem for LANDSAT 7 satellite images, assessed on a variety of in- and out-of-distribution images. Our results successfully match the performance of clean images on a downstream water body segmentation task in Canada. Authors: Alexander Pondaven (Imperial College London); Mart Bakler (Imperial College London); Donghu Guo (Imperial College London); Hamzah Hashim (Imperial College London); Martin G Ignatov (Imperial college London); Samir Bhatt (Imperial College London); Seth Flaxman (Oxford); Swapnil Mishra (Imperial College London); Elie Alhajjar (USMA); Harrison Zhu (Imperial College London)	Computer Vision & Remote Sensing Earth Observation & Monitoring Generative Modeling Meta- and Transfer Learning
NeurIPS 2022	A POMDP Model for Safe Geological Carbon Sequestration (Papers Track) Abstract and authors: (click to expand) Abstract: Geological carbon capture and sequestration (CCS), where CO2 is stored in subsurface formations, is a promising and scalable approach for reducing global emissions.However, if done incorrectly, it may lead to earthquakes and leakage of CO2 back to the surface, harming both humans and the environment. These risks are exacerbated by the large amount of uncertainty in the structure of the storage formation. For these reasons, we propose that CCS operations be modeled as a partially observable Markov decision process (POMDP) and decisions be informed using automated planning algorithms. To this end, we develop a simplified model of CCS operations based on a 2D spillpoint analysis that retains many of the challenges and safety considerations of the real-world problem. We show how off-the-shelf POMDP solvers outperform expert baselines for safe CCS planning. This POMDP model can be used as a test bed to drive the development of novel decision-making algorithms for CCS operations. Authors: Anthony Corso (Stanford University); Yizheng Wang (Stanford Univerity); Markus Zechner (Stanford University); Jef Caers (Stanford University); Mykel J Kochenderfer (Stanford University)	Carbon Capture & Sequestration Reinforcement Learning
NeurIPS 2022	Optimizing Japanese dam reservoir inflow forecast for efficient operation (Papers Track) Abstract and authors: (click to expand) Abstract: Despite a climate and topology favorable to hydropower (HP) generation, HP only accounts for 4% of today’s Japanese primary energy consumption mix. In recent years, calls for improving the efficiency of Japanese HP towards achieving a more sustainable energy mix have emerged from prominent voices in the Ministry of Land, Infrastructure, Transport and Tourism (MILT). Among potential optimizations, data-driven dam operation policies using accurate river discharge forecasts have been advocated for. In the meantime, Machine Learning (ML) has recently made important strides in hydrological modeling, with forecast accuracy improvements demonstrated on both precipitation nowcasting and river discharge prediction. We are motivated by the convergence of these societal and technological contexts: our final goal is to provide scientific evidence and actionable insights for dam infrastructure managers and policy makers to implement more energy-efficient and flood-resistant dam operation policies on a national scale. Towards this goal this work presents a preliminary study of ML-based dam inflow forecasts on a dataset of 127 Japanese public dams we assembled. We discuss our preliminary results and lay out a path for future studies. Authors: Keisuke Yoshimi (Kobe University); Tristan E.M Hascoet (Kobe University); Rousslan F. Julien Dossa (Kobe University); Ryoichi Takashima (Kobe University); Tetsuya Takiguchi (Kobe University); Satoru Oishi (Kobe University)	Climate Science & Modeling Time-series Analysis
NeurIPS 2022	Deep Climate Change: A Dataset and Adaptive domain pre-trained Language Models for Climate Change Related Tasks (Papers Track) Abstract and authors: (click to expand) Abstract: The quantity and quality of literature around climate change (CC) and its impacts are increasing yearly. Yet, this field has received limited attention in the Natural Language Processing (NLP) community. With the help of large Language Models (LMs) and transfer learning, NLP can support policymakers, researchers, and climate activists in making sense of large-scale and complex CC-related texts. CC-related texts include specific language that general language models cannot represent accurately. Therefore we collected a climate change corpus consisting of over 360 thousand abstracts of top climate scientists' articles from trustable sources covering large temporal and spatial scales. Comparison of the performance of GPT2 LM and our 'climateGPT2 models', fine-tuned on the CC-related corpus, on claim generation (text generation) and fact-checking, downstream tasks show the better performance of the climateGPT2 models compared to the GPT2. The climateGPT2 models decrease the validation loss to 1.08 for claim generation from 43.4 obtained by GPT2. We found that climateGPT2 models improved the masked language model objective for the fact-checking task by increasing the F1 score from 0.67 to 0.72. Authors: Saeid Vaghefi (University of Zürich); Veruska Muccione (University of Zürich); Christian Huggel (University of Zürich); Hamed Khashehchi (2w2e GmbH); Markus Leippold (University of Zurich)	Natural Language Processing Meta- and Transfer Learning
NeurIPS 2022	Data-Driven Optimal Solver for Coordinating a Sustainable and Stable Power Grid (Papers Track) Abstract and authors: (click to expand) Abstract: With today's pressing climate change concerns, the widespread integration of low-carbon technologies such as sustainable generation systems (e.g. photovoltaics, wind turbines, etc.) and flexible consumer devices (e.g. storage, electric vehicles, smart appliances, etc.) into the electric grid is vital. Although these power entities can be deployed at large, these are highly variable in nature and must interact with the existing grid infrastructure without violating electrical limits so that the system continues to operate in a stable manner at all times. In order to ensure the integrity of grid operations while also being economical, system operators will need to rapidly solve the optimal power flow (OPF) problem in order to adapt to these fluctuations. Inherent non-convexities in the OPF problem do not allow traditional model-based optimization techniques to offer guarantees on optimality, feasibility and convergence. In this paper, we propose a data-driven OPF solver built on information-theoretic and semi-supervised machine learning constructs. We show that this solver is able to rapidly compute solutions (i.e. in sub-second range) that are within 3\% of optimality with guarantees on feasibility on a benchmark IEEE 118-bus system. Authors: Junfei Wang (York University); Pirathayini Srikantha (York University)	Power & Energy Climate Finance & Economics Unsupervised & Semi-Supervised Learning
NeurIPS 2022	Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation (Papers Track) Abstract and authors: (click to expand) Abstract: How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises coarse-grained high-resolution data. But only keeping this coarse-grained data means the rest of the high-resolution data is thrown out. We use a transfer learning approach, which can be applied to a range of machine learning models, to leverage all the high-resolution data. We use three chaotic systems to show it stabilises training, gives improved generalisation performance and results in better forecasting skill. Our code is at https://github.com/raghul-parthipan/dont_waste_data Authors: Raghul Parthipan (University of Cambridge); Damon Wischik (Univeristy of Cambridge)	Climate Science & Modeling Generative Modeling Meta- and Transfer Learning Time-series Analysis
NeurIPS 2022	Explainable Multi-Agent Recommendation System for Energy-Efficient Decision Support in Smart Homes (Papers Track) Abstract and authors: (click to expand) Abstract: Transparent, understandable, and persuasive recommendations support the electricity consumers’ behavioral change to tackle the energy efficiency problem. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we extend a novel multi-agent approach by designing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models (i.e., k-nearest-neighbours, extreme gradient boosting, adaptive boosting, random forest, logistic regression, and explainable boosting machines). Since we want to help the user understand a single recommendation, we focus on local explainability approaches. In particular, we apply post-model approaches LIME (local, interpretable, model-agnostic explanation) and SHAP (Shapley additive explanations) as model-agnostic tools that can explain the predictions of the chosen classifiers. We further provide an overview of the predictive and explainability performance. Our results show a substantial improvement in the performance of the multi-agent system while at the same time opening up the “black box” of recommendations. To show the pathway to positive impact regarding climate change, we provide a discussion on the potential impact of the suggested approach. Authors: Alona Zharova (Humboldt University of Berlin); Annika Boer (Humboldt University of Berlin); Julia Knoblauch (Humboldt University of Berlin); Kai Ingo Schewina (Humboldt University of Berlin); Jana Vihs (Humboldt University of Berlin)	Recommender Systems Buildings Power & Energy Interpretable ML
NeurIPS 2022	FIRO: A Deep-neural Network for Wildfire Forecast with Interpretable Hidden States (Papers Track) Abstract and authors: (click to expand) Abstract: Several wildfire danger systems have emerged from decades of research. One such system is the National Fire-Danger Rating System (NFDRS), which is used widely across the United States and is a key predictor in the Global ECMWF Fire Forecasting (GEFF) model. The NFDRS is composed of over 100 equations relating wildfire risk to weather conditions, climate and land cover characteristics, and fuel. These equations and the corresponding 130+ parameters were developed via field and lab experiments. These parameters, which are fixed in the standard NFDRS and GEFF implementations, may not be the most appropriate for a climate-changing world. In order to adjust the NFDRS parameters to current climate conditions and specific geographical locations, we recast NFDRS in PyTorch to create a new deep learning-based Fire Index Risk Optimizer (FIRO). FIRO predicts the ignition component, or the probability a wildfire would require suppression in the presence of a firebrand, and calibrates the uncertain parameters for a specific region and climate conditions by training on observed fires. Given the rare occurrence of wildfires, we employed the extremal dependency index (EDI) as the loss function. Using ERA5 reanalysis and MODIS burned area data, we trained FIRO models for California, Texas, Italy, and Madagascar. Across these four geographies, the average EDI improvement was 175% above the standard NFDRS implementation Authors: Eduardo R Rodrigues (MSR); Campbell D Watson (IBM Reserch); Bianca Zadrozny (IBM Research); Gabrielle Nyirjesy (Columbia University)	Disaster Management and Relief Forests Interpretable ML
NeurIPS 2022	Towards a spatially transferable super resolution model for downscaling Antarctic surface melt (Papers Track) Abstract and authors: (click to expand) Abstract: Surface melt on the Antarctic Ice Sheet is an important climate indicator, yet the spatial scale of modeling and observing surface melt is insufficient to capture crucial details and understand local processes. High-resolution climate models could provide a solution, but they are computationally expensive and require finetuning for some model parameters. An alternative method, pioneering in geophysics, is single-image super resolution (SR) applied on lower-resolution model output. However, often input and output of such SR models are available on the same, fixed spatial domain. High-resolution model simulations over Antarctica are available only in some regions. To be able to apply an SR model elsewhere, we propose to make the single-image SR model physics-aware, using surface albedo and elevation as additional input. Our results show a great improvement in the spatial transferability of the conventional SR model. Although issues with the input satellite-derived albedo remain, adding physics awareness paves a way toward a spatially transferable SR model for downscaling Antarctic surface melt. Authors: Zhongyang Hu (IMAU); Yao Sun (TUM); Peter Kuipers Munneke (IMAU); Stef Lhermitte (TU Delft); Xiaoxiang Zhu (Technical University of Munich,Germany)	Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2022	Forecasting European Ozone Air Pollution With Transformers (Papers Track) Abstract and authors: (click to expand) Abstract: Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy to reduce the risk to health, such as air quality warnings. However, forecasting ozone is a difficult problem, as surface ozone concentrations are controlled by a number of physical and chemical processes which act on varying timescales. Accounting for these temporal dependencies appropriately is likely to provide more accurate ozone forecasts. We therefore deploy a state-of-the-art transformer-based model, the Temporal Fusion Transformer, trained on observational station data from three European countries. In four-day test forecasts of daily maximum 8-hour ozone, the novel approach is highly skilful (MAE = 4.6 ppb, R2 = 0.82), and generalises well to two European countries unseen during training (MAE = 4.9 ppb, R2 = 0.79). The model outperforms standard machine learning models on our data, and compares favourably to the published performance of other deep learning architectures tested on different data. We illustrate that the model pays attention to physical variables known to control ozone concentrations, and that the attention mechanism allows the model to use relevant days of past ozone concentrations to make accurate forecasts. Authors: Seb Hickman (University of Cambridge); Paul Griffiths (University of Cambridge); Alex Archibald (University of Cambridge); Peer Nowack (Imperial College London); Elie Alhajjar (USMA)	Climate Science & Modeling Extreme Weather Health Public Policy Societal Adaptation & Resilience Interpretable ML Time-series Analysis
NeurIPS 2022	Stability Constrained Reinforcement Learning for Real-Time Voltage Control (Papers Track) Abstract and authors: (click to expand) Abstract: This paper is a summary of a recently submitted work. Deep Reinforcement Learning (DRL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of explicit stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in both single-phase and three-phase distribution grids. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of our approach with IEEE test feeders, where the proposed method achieves the best overall performance, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability. Authors: Jie Feng (UCSD); Yuanyuan Shi (University of California San Diego); Guannan Qu (Carnegie Mellon University); Steven Low (California Institute of Technology); Animashree Anandkumar (Caltech); Adam Wierman (California Institute of Technology)	Power & Energy Reinforcement Learning
NeurIPS 2022	Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentinel-1 and -2 satellite imagery and develop several extensions toward making it effective in practice. In an application to few-shot Local Climate Zone (LCZ) prediction, we show that these networks outperform multiple common baselines on datasets with a large number of classes. Further, we provide evidence that instance normalization can significantly stabilize the training process and that explicitly shaping the embedding space using supervised contrastive learning can lead to improved performance. Authors: Marcel Hussing (University of Pennsylvania); Karen Li (University of Pennsylvania); Eric Eaton (University of Pennsylvania)	Land Use Meta- and Transfer Learning Cities & Urban Planning Earth Observation & Monitoring
NeurIPS 2022	Exploring Randomly Wired Neural Networks for Climate Model Emulation (Papers Track) Abstract and authors: (click to expand) Abstract: Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts, but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this manuscript, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them to their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired ones and assess the impact on model performance for models with 1 million and 10 million parameters. We find average performance improvements of 4.2% across model complexities and prediction tasks, with substantial performance improvements of up to 16.4% in some cases. Furthermore, we find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models. Authors: William J Yik (Harvey Mudd College); Sam J Silva (The University of Southern California); Andrew Geiss (Pacific Northwest National Laboratory); Duncan Watson-Parris (University of Oxford)	Climate Science & Modeling Earth Observation & Monitoring Extreme Weather Computer Vision & Remote Sensing Time-series Analysis
NeurIPS 2022	Closing the Domain Gap -- Blended Synthetic Imagery for Climate Object Detection (Papers Track) Abstract and authors: (click to expand) Abstract: Object detection models have great potential to increase both the frequency and cost-efficiency of assessing climate-relevant infrastructure in satellite imagery. However, model performance can suffer when models are applied to stylistically different geographies. We propose a technique to generate synthetic imagery using minimal labeled examples of the target object at a low computational cost. Our technique blends example objects onto unlabeled images of the target domain. We show that including these synthetic images improves the average precision of a YOLOv3 object detection model when compared to a baseline and other popular domain adaptation techniques. Authors: Caleb Kornfein (Duke University); Frank Willard (Duke University); Caroline Tang (Duke University); Yuxi Long (Duke University); Saksham Jain (Duke University); Jordan Malof (Duke University); Simiao Ren (Duke University); Kyle Bradbury (Duke University)	Computer Vision & Remote Sensing Meta- and Transfer Learning
NeurIPS 2022	SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications (Papers Track) Abstract and authors: (click to expand) Abstract: The lack of standardized benchmarks for reinforcement learning (RL) in sustainability applications has made it difficult to both track progress on specific domains and identify bottlenecks for researchers to focus their efforts on. In this paper, we present SustainGym, a suite of two environments designed to test the performance of RL algorithms on realistic sustainability tasks. The first environment simulates the problem of scheduling decisions for a fleet of electric vehicle (EV) charging stations, and the second environment simulates decisions for a battery storage system bidding in an electricity market. We describe the structure and features of the environments and show that standard RL algorithms have significant room for improving performance. We discuss current challenges in introducing RL to real-world sustainability tasks, including physical constraints and distribution shift. Authors: Christopher Yeh (California Institute of Technology); Victor Li (California Institute of Technology); Rajeev Datta (California Institute of Technology); Yisong Yue (Caltech); Adam Wierman (California Institute of Technology)	Reinforcement Learning Power & Energy
NeurIPS 2022	Remote estimation of geologic composition using interferometric synthetic-aperture radar in California’s Central Valley (Papers Track) Abstract and authors: (click to expand) Abstract: California's Central Valley is the national agricultural center, producing 1/4 of the nation’s food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. In this study, we aim to identify specific regions with different temporal dynamics of land displacement and find relationships with underlying geological composition. Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture radar (InSAR)-based land deformation temporal changes using machine learning techniques. We identified regions with different temporal characteristics of land displacement in that some areas (e.g., Helm) with coarser grain geologic compositions exhibited potentially reversible land deformation (elastic land compaction). We found a significant correlation between InSAR-based land deformation and geologic composition using random forest and deep neural network regression models. We also achieved significant accuracy with 1/4 sparse sampling to reduce any spatial correlations among data, suggesting that the model has the potential to be generalized to other regions for indirect estimation of geologic composition. Our results indicate that geologic composition can be estimated using InSAR-based land deformation data. In-situ measurements of geologic composition can be expensive and time consuming and may be impractical in some areas. The generalizability of the model sheds light on high spatial resolution geologic composition estimation utilizing existing measurements. Authors: Kyongsik Yun (California Institute of Technology); Kyra Adams (California Institute of Technology); John Reager (California Institute of Technology); Zhen Liu (California Institute of Technology); Caitlyn Chavez (California Institute of Technology); Michael Turmon (California Institute of Technology); Thomas Lu (California Institute of Technology)	Earth Observation & Monitoring Interpretable ML
NeurIPS 2022	AutoML for Climate Change: A Call to Action (Papers Track) Abstract and authors: (click to expand) Abstract: The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate change ML (CCML) community works on a diverse, challenging set of problems which often involve physics-constrained ML or heterogeneous spatiotemporal data. It would be desirable to use automated machine learning (AutoML) techniques to automatically find high-performing architectures and hyperparameters for a given dataset. In this work, we benchmark popular Auto ML libraries on three high-leverage CCML applications: climate modeling, wind power forecasting, and catalyst discovery. We find that out-of-the-box AutoML libraries currently fail to meaningfully surpass the performance of human-designed CCML models. However, we also identify a few key weaknesses, which stem from the fact that most AutoML techniques are tailored to computer vision and NLP applications. For example, while dozens of search spaces have been designed for image and language data, none have been designed for spatiotemporal data. Addressing these key weaknesses can lead to the discovery of novel architectures that yield substantial performance gains across numerous CCML applications. Therefore, we present a call to action to the AutoML community, since there are a number of concrete, promising directions for future work in the space of AutoML for CCML. We release our code and a list of resources at https://github.com/climate-change-automl/climate-change-automl. Authors: Renbo Tu (University of Toronto); Nicholas Roberts (University of Wisconsin-Madison); Vishak Prasad C (Indian Institute Of Technology, Bombay); Sibasis Nayak (Indian Institute of Technology, Bombay); Paarth Jain (Indian Institute of Technology Bombay); Frederic Sala (University of Wisconsin-Madison); Ganesh Ramakrishnan (IIT Bombay); Ameet Talwalkar (CMU); Willie Neiswanger (Stanford University); Colin White (Abacus.AI)	Climate Science & Modeling Chemistry & Materials Power & Energy
NeurIPS 2022	Temperature impacts on hate speech online: evidence from four billion tweets (Papers Track) Abstract and authors: (click to expand) Abstract: Human aggression is no longer limited to the physical space but exists in the form of hate speech on social media. Here, we examine the effect of temperature on the occurrence of hate speech on Twitter and interpret the results in the context of climate change, human behavior and mental health. Employing supervised machine learning models, we identify hate speech in a data set of four billion geolocated tweets from over 750 US cities (2014 – 2020). We statistically evaluate the changes in daily hate tweets against changes in local temperature, isolating the temperature influence from confounding factors using binned panel-regression models. We find a low prevalence of hate tweets in moderate temperatures and observe sharp increases of up to 12% for colder and up to 22% for hotter temperatures, indicating that not only hot but also cold temperatures increase aggressive tendencies. Further, we observe that for extreme temperatures hate speech also increases as a percentage of total tweeting activity, crowding out non-hate speech. The quasi-quadratic shape of the temperature-hate tweet curve is robust across varying climate zones, income groups, religious and political beliefs. The prevalence of the results across climatic and socioeconomic splits points to limits in adaptation. Our results illuminate hate speech online as an impact channel through which temperature alters societal aggression. Authors: Annika Stechemesser (Potsdam Insitute for Climate Impact Research); Anders Levermann (Potsdam Institute for Climate Impact Research); Leonie Wenz (Potsdam Institute for Climate Impact Research)	Health Societal Adaptation & Resilience Data Mining Natural Language Processing
NeurIPS 2022	Cross Modal Distillation for Flood Extent Mapping (Papers Track) Abstract and authors: (click to expand) Abstract: The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection module of an operational early flood warning system. Our method exploits an unlabelled dataset of paired multi-spectral and Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a purely supervised learning method. Past attempts have used such unlabelled data by creating weak labels out of them, but end up learning the label mistakes in those weak labels. Motivated by knowledge distillation and semi supervised learning, we explore the use of a teacher to train a student with the help of a small hand labeled dataset and a large unlabelled dataset. Unlike the conventional self distillation setup, we propose a cross modal distillation framework that transfers supervision from a teacher trained on richer modality (multi-spectral images) to a student model trained on SAR imagery. The trained models are then tested on the Sen1Floods11 dataset. Our model outperforms the Sen1Floods11 SAR baselines by an absolute margin of 4.15% pixel wise Intersection-over-Union (IoU) on the test split. Authors: Shubhika Garg (Google); Ben Feinstein (Google); Shahar Timnat (Google); Vishal V Batchu (Google); gideon dror (The Academic College of Tel-Aviv-Yaffo); Adi Gerzi Rosenthal (Google); Varun Gulshan (Google Research)	Computer Vision & Remote Sensing Disaster Management and Relief Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2022	Transformer Neural Networks for Building Load Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate electrical load forecasts of buildings are needed to optimize local energy storage and to make use of demand-side flexibility. We study the usage of Transformer neural networks for short-term electrical load forecasting of 296 buildings from a public dataset. Transformer neural networks trained on many buildings give the best forecasts on 115 buildings, and multi-layer perceptrons trained on a single building are better on 161 buildings. In addition, we evaluate the models on buildings that were not used for training, and find that Transformer neural networks generalize better than multi-layer perceptrons and our statistical baselines. This shows that the usage of Transformer neural networks for building load forecasting could reduce training resources due to the good generalization to unseen buildings, and they could be useful for cold-start scenarios. Authors: Matthias Hertel (KIT); Simon Ott (KIT); Oliver Neumann (KIT); Benjamin Schäfer (KIT); Ralf Mikut (Karlsruhe Institute of Technology); Veit Hagenmeyer (Karlsruhe Institute of Technology (KIT))	Time-series Analysis Buildings
NeurIPS 2022	Estimating Chicago’s tree cover and canopy height using multi-spectral satellite imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Information on urban tree canopies is fundamental to mitigating climate change as well as improving quality of life. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago. Authors: John Francis (University College London)	Cities & Urban Planning Ecosystems & Biodiversity Public Policy Computer Vision & Remote Sensing
NeurIPS 2022	Reconstruction of Grid Measurements in the Presence of Adversarial Attacks (Papers Track) Abstract and authors: (click to expand) Abstract: In efforts to mitigate the adverse effects of climate change, policymakers have set ambitious goals to reduce the carbon footprint of all sectors - including the electric grid. To facilitate this, sustainable energy systems like renewable generation must { be} deployed at high numbers throughout the grid. As these are highly variable in nature, the grid must be closely monitored so that system operators will have elevated situational awareness and can execute timely actions to maintain stable grid operations. With the widespread deployment of sensors like phasor measurement units (PMUs), an abundance of data is available for conducting accurate state estimation. However, due to the cyber-physical nature of the power grid, measurement data can be perturbed in an adversarial manner to enforce incorrect decision-making. In this paper, we propose a novel reconstruction method that leverages on machine learning constructs like CGAN and gradient search to recover the original states when subjected to adversarial perturbations. Experimental studies conducted on the practical IEEE 118-bus benchmark power system show that the proposed method can reduce errors due to perturbation by large margins (i.e. up to 100%). Authors: Amirmohammad Naeini (York University); Samer El Kababji (Western University); Pirathayini Srikantha (York University)	Generative Modeling Unsupervised & Semi-Supervised Learning
NeurIPS 2022	Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble (Papers Track) Abstract and authors: (click to expand) Abstract: One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables such as weather, and holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417kW for RMSE is achieved with the proposed framework in comparison to all other methods. Authors: Satyaki Chatterjee (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Adithya Ramachandran (Pattern Recognition Lab, Friedrich Alexander University, Erlangen); Thorkil Flensmark Neergaard (Brønderslev Forsyning A/S); Andreas K Maier (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Siming Bayer (Pattern Recognition Lab, Friedrich-Alexander University)	Time-series Analysis Cities & Urban Planning Power & Energy Interpretable ML
NeurIPS 2022	Generalized Ice Detection on Wind Turbine Rotor Blades with Neural Style Transfer (Papers Track) Abstract and authors: (click to expand) Abstract: Wind energy’s ability to liberate the world of conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies focus on specific wind parks and fail to generalize to unseen scenarios (e.g. new rotor blade designs). We propose the utilisation of synthetic data augmentation via neural style transfer to improve the generalization of existing ice prediction models. We show that training models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable towards tackling climate change. Authors: Joyjit Chatterjee (University of Hull); Maria Teresa Alvela Nieto (University of Bremen); Hannes Gelbhardt (University of Bremen); Nina Dethlefs (University of Hull); Jan Ohlendorf (University of Bremen); Klaus-Dieter Thoben (University of Bremen)	Power & Energy Computer Vision & Remote Sensing Meta- and Transfer Learning
NeurIPS 2022	Identifying Compound Climate Drivers of Forest Mortality with β-VAE (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is expected to lead to higher rates of forest mortality. Forest mortality is a complex phenomenon driven by the interaction of multiple climatic variables at multiple temporal scales, further modulated by the current state of the forest (e.g. age, stem diameter, and leaf area index). Identifying the compound climate drivers of forest mortality would greatly improve understanding and projections of future forest mortality risk. Observation data are, however, limited in accuracy and sample size, particularly in regard to forest state variables and mortality events. In contrast, simulations with state-of-the-art forest models enable the exploration of novel machine learning techniques for associating forest mortality with driving climate conditions. Here we simulate 160,000 years of beech, pine and spruce forest dynamics with the forest model FORMIND. We then apply β-VAE to learn disentangled latent representations of weather conditions and identify those that are most likely to cause high forest mortality. The learned model successfully identifies three characteristic climate representations that can be interpreted as different compound drivers of forest mortality. Authors: Mohit Anand (Helmholtz Centre for Environmental Research - UFZ); Lily-belle Sweet (Helmholtz Centre for Environmental Research - UFZ); Gustau Camps-Valls (Universitat de València); Jakob Zscheischler (Helmholtz Centre for Environmental Research - UFZ)	Generative Modeling Climate Science & Modeling Earth Observation & Monitoring Ecosystems & Biodiversity Extreme Weather Forests Interpretable ML Time-series Analysis
NeurIPS 2022	TCFD-NLP: Assessing alignment of climate disclosures using NLP for the financial markets (Papers Track) Abstract and authors: (click to expand) Abstract: Climate-related disclosure is increasing in importance as companies and stakeholders alike aim to reduce their environmental impact and exposure to climate-induced risk. Companies primarily disclose this information in annual or other lengthy documents where climate information is not the sole focus. To assess the quality of a company's climate-related disclosure, these documents, often hundreds of pages long, must be reviewed manually by climate experts. We propose a more efficient approach to assessing climate-related financial information. We construct a model leveraging TF-IDF, sentence transformers and multi-label k nearest neighbors (kNN). The developed model is capable of assessing alignment of climate disclosures at scale, with a level of granularity and transparency that will support decision-making in the financial markets with relevant climate information. In this paper, we discuss the data that enabled this project, the methodology, and how the resulting model can drive climate impact. Authors: Rylen Sampson (Manifest Climate); Aysha Cotterill (Manifest Climate); Quoc Tien Au (Manifest Climate)	Climate Finance & Economics Natural Language Processing
NeurIPS 2022	Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes (Papers Track) Abstract and authors: (click to expand) Abstract: With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent from weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslide events obtained from several Sentinel-1 satellite passes before and after a landslide triggering event together with segmentation maps of the landslides. From this dataset, using the Hokkaido, Japan datacube, we study the feasibility of SAR-based landslide detection with supervised deep learning (DL). Our results demonstrate that DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7. We find that additional satellite visits enhance detection performance, but that early detection is possible when SAR data is combined with terrain information from a digital elevation model. This can be especially useful for time-critical emergency interventions. Authors: Vanessa Boehm (UC Berkeley); Wei Ji Leong (The Ohio State University); Ragini Bal Mahesh (German Aerospace Center DLR); Ioannis Prapas (National Observatory of Athens); Siddha Ganju (Nvidia); Freddie Kalaitzis (University of Oxford); Edoardo Nemni (United Nations Satellite Centre (UNOSAT)); Raul Ramos-Pollan (Universidad de Antioquia)	Disaster Management and Relief Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022	Hybrid Recurrent Neural Network for Drought Monitoring (Papers Track) Abstract and authors: (click to expand) Abstract: Droughts are pervasive hydrometeorological phenomena and global hazards, whose frequency and intensity are expected to increase in the context of climate change. Drought monitoring is of paramount relevance. Here we propose a hybrid model for drought detection that integrates both climatic indices and data-driven models in a hybrid deep learning approach. We exploit time-series of multi-scale Standardized Precipitation Evapotranspiration Index together with precipitation and temperature as inputs. We introduce a dual-branch recurrent neural network with convolutional lateral connections for blending the data. Experimental and ablative results show that the proposed system outperforms both the considered drought index and purely data-driven deep learning models. Our results suggest the potential of hybrid models for drought monitoring and open the door to synergistic systems that learn from data and domain knowledge altogether. Authors: Mengxue Zhang (Universitat de València); Miguel-Ángel Fernández-Torres (Universitat de València); Gustau Camps-Valls (Universitat de València)	Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Hybrid Physical Models Time-series Analysis
NeurIPS 2022	Deep Learning for Global Wildfire Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns. Authors: Ioannis Prapas (National Observatory of Athens); Akanksha Ahuja (NOA); Spyros Kondylatos (National Observatory of Athens); Ilektra Karasante (National Observatory of Athens); Lazaro Alonso (Max Planck Institute for Biogeochemistry); Eleanna Panagiotou (Harokopio University of Athens); Charalampos Davalas (Harokopio University of Athens); Dimitrios Michail (Harokopio University of Athens); Nuno Carvalhais (Max Planck Institute for Biogeochemistry); Ioannis Papoutsis (National Observatory of Athens)	Earth Observation & Monitoring Climate Science & Modeling Disaster Management and Relief Extreme Weather Computer Vision & Remote Sensing
NeurIPS 2022	Causal Modeling of Soil Processes for Improved Generalization (Papers Track) Abstract and authors: (click to expand) Abstract: Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work in soil organic carbon estimation, current approaches do not generalize well across soil conditions and management practices. We empirically show that explicit modeling of cause-and-effect relationships among the soil processes improves the out-of-distribution generalizability of prediction models. We provide a comparative analysis of soil organic carbon estimation models where the skeleton is estimated using causal discovery methods. Our framework provide an average improvement of 81% in test mean squared error and 52% in test mean absolute error. Authors: Somya Sharma (U. Minnesota); Swati Sharma (Microsoft Research); Emre Kiciman (Microsoft Research); Andy Neal (Rothamstead); Ranveer Chandra (Microsoft Research); John Crawford (University of Glasgow); Sara Malvar (Microsoft); Eduardo R Rodrigues (MSR)	Agriculture & Food Causal & Bayesian Methods
NeurIPS 2022	Machine Learning for Activity-Based Road Transportation Emissions Estimation (Papers Track) Abstract and authors: (click to expand) Abstract: Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives towards meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework. Authors: Derek Rollend (JHU); Kevin Foster (JHU); Tomek Kott (JHU); Rohita Mocharla (JHU); Rodrigo Rene Rai Munoz Abujder (Johns Hopkins Applied Physics Laboratory); Neil Fendley (JHU/APL); Chace Ashcraft (JHU/APL); Frank Willard (JHU); Marisa Hughes (JHU)	Transportation Cities & Urban Planning Computer Vision & Remote Sensing
NeurIPS 2022	Bayesian State-Space SCM for Deforestation Baseline Estimation for Forest Carbon Credit (Papers Track) Best Paper: Pathway to Impact Abstract and authors: (click to expand) Abstract: In forest carbon credit, the concept of dynamic (or ex-post) baseline has been discussed to overcome the criticism of junk carbon credit, while an ex-ante baseline is still necessary in terms of project finance and risk assessment. We propose a Bayesian state-space SCM, which integrates both ex-ante and ex-post baseline estimation in a time-series causal inference framework. We apply the proposed model to a REDD+ project in Brazil, and show that it might have had a small, positive effect but had been over-credited and that the 90% predictive interval of the ex-ante baseline included the ex-post baseline, implying our ex-ante estimation can work effectively. Authors: Keisuke Takahata (sustainacraft, Inc.); Hiroshi Suetsugu (sustainacraft, Inc.); Keiichi Fukaya (National Institute for Environmental Studies); Shinichiro Shirota (Hitotsubashi University)	Forests Causal & Bayesian Methods Land Use
NeurIPS 2022	Estimating Corporate Scope 1 Emissions Using Tree-Based Machine Learning Methods (Papers Track) Abstract and authors: (click to expand) Abstract: Companies worldwide contribute to climate change, emitting significant amounts of greenhouse gases (GHGs). Yet, most do not report their direct or Scope 1 emissions, resulting in a large data gap in corporate emissions. This study aims to fill this gap by training several decision-tree machine learning models to predict company-level Scope 1 emissions. Our results demonstrate that the Extreme Gradient Boosting and LightGBM models perform best, where the former shows a 19% improvement in prediction error over a benchmark model. Our model is also of reduced complexity and greater computational efficiency; it does not require meta-learners and is trained on a smaller number of features, for which data is more common and accessible compared to prior works. Our features are uniquely chosen based on concepts of environmental pollution in economic theory. Predicting corporate emissions with machine learning can be used as a gap-filling approach, which would allow for better GHG accounting and tracking, thus facilitating corporate decarbonization efforts in the long term. It can also impact representations of a company’s carbon performance and carbon risks, thereby helping to funnel investments towards companies with lower emissions and those making true efforts to decarbonize. Authors: Elham Kheradmand (University of Montreal); Maida Hadziosmanovic (Concordia University); Nazim Benguettat (Concordia); H. Damon Matthews (Concordia University); Shannon M. Lloyd (Concordia University)	Climate Finance & Economics Interpretable ML
NeurIPS 2022	Analyzing Micro-Level Rebound Effects of Energy Efficient Technologies (Papers Track) Abstract and authors: (click to expand) Abstract: Energy preservation is central to prevent resource depletion, climate change and environment degradation. Investment in raising efficiency of appliances is among the most significant attempts to save energy. Ironically, introduction of many such energy saving appliances increased the total energy consumption instead of reducing it. This effect in literature is attributed to the inherent Jevons paradox (JP) and optimism bias (OB) in consumer behavior. However, the magnitude of these instincts vary among different people. Identification of this magnitude for each household can enable the development of appropriate policies that induce desired energy saving behaviour. Using the RECS 2015 dataset, the paper uses machine learning for each electrical appliance to determine the dependence of their total energy consumption on their energy star rating. This shows that only substitutable appliances register increase in energy demand upon boosted efficiency. Lastly, an index is noted to indicate the varying influence of JP and OB on different households. Authors: Mayank Jain (University College Dublin); Mukta Jain (Delhi School of Economics); Tarek T. Alskaif (Wageningen University); Soumyabrata Dev (University College Dublin)	Behavioral and Social Science Cities & Urban Planning Power & Energy
NeurIPS 2022	Comparing the carbon costs and benefits of low-resource solar nowcasting (Papers Track) Abstract and authors: (click to expand) Abstract: Mitigating emissions in line with climate goals requires the rapid integration of low carbon energy sources, such as solar photovoltaics (PV) into the electricity grid. However, the energy produced from solar PV fluctuates due to clouds obscuring the sun's energy. Solar PV yield nowcasting is used to help anticipate peaks and troughs in demand to support grid integration. This paper compares multiple low-resource approaches to nowcasting solar PV yield. To do so, we use a dataset of UK satellite imagery and solar PV energy readings over a 1 to 4-hour time range. Our work investigates the performance of multiple nowcasting models. The paper also estimates the carbon emissions generated and averted by deploying models such as these, and finds that short-term PV forecasting may have a benefit several orders of magnitude greater than its carbon cost and that this benefit holds for small models that could be deployable in low-resource settings around the globe. Authors: Ben Dixon (UCL); Jacob Bieker (Open Climate Fix); Maria Perez-Ortiz (University College London)	Computer Vision & Remote Sensing Climate Science & Modeling Time-series Analysis
NeurIPS 2022	Climate Policy Tracker: Pipeline for automated analysis of public climate policies (Papers Track) Abstract and authors: (click to expand) Abstract: The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing. The documents are long and tedious for manual analysis, especially for policy experts, lawmakers, and citizens who lack access or domain expertise to utilize data analytics tools. Potential consequences of such a situation include reduced citizen governance and involvement in climate policies and an overall surge in analytics costs, rendering less accessibility for the public. In this work, we use a Latent Dirichlet Allocation-based pipeline for the automatic summarization and analysis of 10-years of national energy and climate plans (NECPs) for the period from 2021 to 2030, established by 27 Member States of the European Union. We focus on analyzing policy framing, the language used to describe specific issues, to detect essential nuances in the way governments frame their climate policies and achieve climate goals. The methods leverage topic modeling and clustering for the comparative analysis of policy documents across different countries. It allows for easier integration in potential user-friendly applications for the development of theories and processes of climate policy. This would further lead to better citizen governance and engagement over climate policies and public policy research. Authors: Artur Żółkowski (Warsaw University of Technology); Mateusz Krzyziński (Warsaw University of Technology); Piotr Wilczyński (Warsaw University of Technology); Stanisław Giziński (University of Warsaw); Emilia Wiśnios (University of Warsaw); Bartosz Pieliński (University of Warsaw); Julian Sienkiewicz (Warsaw University of Technology); Przemysław Biecek (Warsaw University of Technology)	Public Policy Natural Language Processing
NeurIPS 2022	Topic correlation networks inferred from open-ended survey responses reveal signatures of ideology behind carbon tax opinion (Papers Track) Abstract and authors: (click to expand) Abstract: Ideology can often render policy design ineffective by overriding what, at face value, are rational incentives. A timely example is carbon pricing, whose public support is strongly influenced by ideology. As a system of ideas, ideology expresses itself in the way people explain themselves and the world. As an object of study, ideology is then amenable to a generative modelling approach within the text-as-data paradigm. Here, we analyze the structure of ideology underlying carbon tax opinion using topic models. An idea, termed a topic, is operationalized as the fixed set of proportions with which words are used when talking about it. We characterize ideology through the relational structure between topics. To access this latent structure, we use the highly expressive Structural Topic Model to infer topics and the weights with which individual opinions mix topics. We fit the model to a large dataset of open-ended survey responses of Canadians elaborating on their support of or opposition to the tax. We propose and evaluate statistical measures of ideology in our data, such as dimensionality and heterogeneity. Finally, we discuss the implications of the results for transition policy in particular, and of our approach to analyzing ideology for computational social science in general. Authors: Maximilian Puelma Touzel (Mila)	Behavioral and Social Science Climate Finance & Economics Public Policy Generative Modeling Natural Language Processing
NeurIPS 2022	Controllable Generation for Climate Modeling (Papers Track) Abstract and authors: (click to expand) Abstract: Recent years have seen increased interest in modeling future climate trends, especially from the point of view of accurately predicting, understanding and mitigating downstream impacts. For instance, current state-of-the-art process-based agriculture models rely on high-resolution climate data during the growing season for accurate estimation of crop yields. However, high-resolution climate data for future climates is unavailable and needs to be simulated, and that too for multiple possible climate scenarios, which becomes prohibitively expensive via traditional methods. Meanwhile, deep generative models leveraging the expressivity of neural networks have shown immense promise in modeling distributions in high dimensions. Here, we cast the problem of simulation of climate scenarios in a generative modeling framework. Specifically, we leverage the GAN (Generative Adversarial Network) framework for simulating synthetic climate scenarios. We condition the model by quantifying the degree of ``extremeness" of the observed sample, which allows us to sample from different parts of the distribution. We demonstrate the efficacy of the proposed method on the CHIRPS precipitation dataset. Authors: Moulik Choraria (University of Illinois at Urbana-Champaign); Daniela Szwarcman (IBM Research); Bianca Zadrozny (IBM Research); Campbell D Watson (IBM Reserch); Lav Varshney (UIUC: ECE)	Climate Science & Modeling Extreme Weather Generative Modeling
NeurIPS 2022	Learn to Bid: Deep Reinforcement Learning with Transformer for Energy Storage Bidding in Energy and Contingency Reserve Markets (Papers Track) Abstract and authors: (click to expand) Abstract: As part of efforts to tackle climate change, grid-scale battery energy storage systems (BESS) play an essential role in facilitating reliable and secure power system operation with variable renewable energy (VRE). BESS can balance time-varying electricity demand and supply in the spot market through energy arbitrage and in the frequency control ancillary services (FCAS) market through service enablement or delivery. Effective algorithms are needed for the optimal participation of BESS in multiple markets. Using deep reinforcement learning (DRL), we present a BESS bidding strategy in the joint spot and contingency FCAS markets, leveraging a transformer-based temporal feature extractor to exploit the temporal trends of volatile energy prices. We validate our strategy on real-world historical energy prices in the Australian National Electricity Market (NEM). We demonstrate that the novel DRL-based bidding strategy significantly outperforms benchmarks. The simulation also reveals that the joint bidding in both the spot and contingency FCAS markets can yield a much higher profit than in individual markets. Our work provides a viable use case for the BESS, contributing to the power system operation with high penetration of renewables. Authors: Jinhao Li (Monash University); Changlong Wang (Monash University); Yanru Zhang (University of Electronic Science and Technology of China); Hao Wang (Monash University)	Power & Energy Reinforcement Learning
NeurIPS 2022	Curriculum Based Reinforcement Learning to Avert Cascading Failures in the Electric Grid (Papers Track) Abstract and authors: (click to expand) Abstract: We present an approach to integrate the domain knowledge of the electric power grid operations into reinforcement learning (RL) frameworks for effectively learning RL agents to prevent cascading failures. A curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using the network physics. Our procedure is tested on an actor-critic-based agent on the IEEE 14-bus test system using the RL environment developed by RTE, the French transmission system operator (TSO). We observed that naively training the RL agent without the curriculum approach failed to prevent cascading for most test scenarios, while the curriculum based RL agents succeeded in most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL applications. Authors: Amarsagar Reddy Ramapuram Matavalam (Arizona State University); Kishan Guddanti (Pacific Northwest National Lab); Yang Weng (Arizona State University)	Power & Energy Reinforcement Learning
NeurIPS 2022	Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction (Papers Track) Abstract and authors: (click to expand) Abstract: Precipitation drives the hydroclimate of Earth and its spatiotemporal changes on a day to day basis have one of the most notable socioeconomic impacts. The success of numerical weather prediction (NWP) is measured by the improvement of forecasts for various physical fields such as temperature and pressure. Large biases however exist in the precipitation predictions. Pure deep learning based approaches lack the advancements acheived by NWP in the past two to three decades. Hybrid methodology using NWP outputs as inputs to the deep learning based refinement tool offer an attractive means taking advantage of both NWP and state of the art deep learning algorithms. Augmenting the output from a well-known NWP model: Coupled Forecast System ver.2 (CFSv2) with deep learning for the first time, we demonstrate a hybrid model capability (DeepNWP) which shows substantial skill improvements for short-range global precipitation at 1-, 2- and 3-days lead time. To achieve this hybridization, we address the sphericity of the global data by using modified DLWP-CS architecture which transforms all the fields to cubed-sphere projection. The dynamical model outputs corresponding to precipitation and surface temperature are ingested to a UNET for predicting the target ground truth precipitation. While the dynamical model CFSv2 shows a bias in the range of +5 to +7 mm/day over land, the multivariate deep learning model reduces it to -1 to +1 mm/day over global land areas. We validate the results by taking examples from Hurricane Katrina in 2005, Hurricane Ivan in 2004, Central European floods in 2010, China floods in 2010, India floods in 2005 and the Myanmar cyclone Nargis in 2008. Authors: Manmeet Singh (The University of Texas at Austin); Vaisakh SB (Indian Institute of Tropical Meteorology); Nachiketa Acharya (Department of Meteorology and Atmospheric Science,Pennsylvania State University); Aditya Grover (UCLA); Suryachandra A. Rao (Indian Institute of Tropical Meteorology); Bipin Kumar (Indian Institute of Tropical Meteorology); Zong-Liang Yang (The University of Texas at Austin); Dev Niyogi (The University of Texas at Austin)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2022	A Multi-Scale Deep Learning Framework for Projecting Weather Extremes (Papers Track) Best Paper: ML Innovation Abstract and authors: (click to expand) Abstract: Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. Under climate change, their impact and intensity are expected to worsen significantly. Unfortunately, general circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize weather extremes accurately. To address this, we present a multi-resolution deep-learning framework that, firstly, corrects a GCM's biases by matching low-order and tail statistics of its output with observations at coarse scales; and secondly, increases the level of detail of the debiased GCM output by reconstructing the finer scales as a function of the coarse scales. We use the proposed framework to generate statistically realistic realizations of the climate over Western Europe from a simple GCM corrected using observational atmospheric reanalysis. We also discuss implications for probabilistic risk assessment of natural disasters in a changing climate. Authors: Antoine Blanchard (MIT); Nishant Parashar (Verisk Analytics); Boyko Dodov (Verisk Analytics); Christian Lessig (Otto-von-Guericke-Universitat Magdeburg); Themis Sapsis (MIT)	Extreme Weather Hybrid Physical Models
NeurIPS 2022	A Global Classification Model for Cities using ML (Papers Track) Abstract and authors: (click to expand) Abstract: This paper develops a novel data set for three key resources use; namely, food, water, and energy, for 9000 cities globally. The data set is then utilized to develop a clustering approach as a starting point towards a global classification model. This novel clustering approach aims to contribute to developing an inclusive view of resource efficiency for all urban centers globally. The proposed clustering algorithm is comprised of three steps: first, outlier detection to address specific city characteristics, then a Variational Autoencoder (VAE), and finally, Agglomerative Clustering (AC) to improve the classification results. Our results show that this approach is more robust and yields better results in creating delimited clusters with high Calinski-Harabasz Index scores and Silhouette Coefficient than other baseline clustering methods. Authors: Doron Hazan (MIT); Mohamed Habashy (Massachusetts Institute of Technology); Mohanned ElKholy (Massachusetts Institute of Technology); Omer Mousa (American University in Cairo); Norhan M Bayomi (MIT Environmental Solutions Initiative); Matias Williams (Massachusetts Institute of Technology); John Fernandez (Massachusetts Institute of Technology)	Cities & Urban Planning Data Mining
NeurIPS 2022	EnhancedSD: Downscaling Solar Irradiance from Climate Model Projections (Papers Track) Abstract and authors: (click to expand) Abstract: Renewable energy-based electricity systems are seen as a keystone of future decarbonization efforts. However, power system planning does not currently consider the impacts of climate change on renewable energy resources such as solar energy, chiefly due to a paucity of climate-impacted high-resolution solar power data. Existing statistical downscaling (SD) methods that learn to map coarse-resolution versions of historical reanalysis data to generate finer resolution outputs are of limited use when applied to future climate model projections due to the domain gap between climate models and reanalysis data. In contrast, we present EnhancedSD, a deep learning-based framework for downscaling coarse-scale climate model outputs to high-resolution observational (reanalysis) data. Our proposed ML based downscaling allows for future reanalysis projections, which can be pivotal for mitigating climate change’s impacts on power systems planning. Authors: Nidhin Harilal (University of Colorado, Boulder); Bri-Mathias S Hodge (University of Colorado Boulder); Claire Monteleoni (University of Colorado Boulder); Aneesh Subramanian (University of California, San Diego)	Computer Vision & Remote Sensing Climate Science & Modeling Power & Energy
NeurIPS 2022	Positional Encoder Graph Neural Networks for Geographic Data (Papers Track) Abstract and authors: (click to expand) Abstract: Modeling spatial dependencies in geographic data is of crucial importance for the modeling of our planet. Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, in the absence of further context on the geometric structure of the data, they often rely on Euclidean distances to construct the input graphs. This assumption can be improbable in many real-world settings, where the spatial structure is more complex and explicitly non-Euclidean (e.g., road networks). In this paper, we propose PE-GNN, a new framework that incorporates spatial context and correlation explicitly into the models. Building on recent advances in geospatial auxiliary task learning and semantic spatial embeddings, our proposed method (1) learns a context-aware vector encoding of the geographic coordinates and (2) predicts spatial autocorrelation in the data in parallel with the main task. We show the effectiveness of our approach on two climate-relevant regression tasks: 3d spatial interpolation and air temperature prediction. The code for this study can be accessed via: https://bit.ly/3xDpfyV. Authors: Konstantin Klemmer (Microsoft Research); Nathan S Safir (University of Georgia); Daniel B Neill (New York University)	Climate Science & Modeling Earth Observation & Monitoring Extreme Weather
NeurIPS 2022	Image-based Early Detection System for Wildfires (Papers Track) Abstract and authors: (click to expand) Abstract: Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person. Authors: Omkar Ranadive (Alchera X); Jisu Kim (Alchera); Serin Lee (Alchera X); Youngseo Cha (Alchera); Heechan Park (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera)	Computer Vision & Remote Sensing Disaster Management and Relief
NeurIPS 2022	Towards Global Crop Maps with Transfer Learning (Papers Track) Abstract and authors: (click to expand) Abstract: The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is need for timely, large-scale and precise mapping of crops for evidence-based decision making. A key enabler towards this direction are new satellite missions that freely offer big remote sensing data of high spatio-temporal resolution and global coverage. During the previous decade and because of this surge of big Earth observations, deep learning methods have dominated the remote sensing and crop mapping literature. Nevertheless, deep learning models require large amounts of annotated data that are scarce and hard-to-acquire. To address this problem, transfer learning methods can be used to exploit available annotations and enable crop mapping for other regions, crop types and years of inspection. In this work, we have developed and trained a deep learning model for paddy rice detection in South Korea using Sentinel-1 VH time-series. We then fine-tune the model for i) paddy rice detection in France and Spain and ii) barley detection in the Netherlands. Additionally, we propose a modification in the pre-trained weights in order to incorporate extra input features (Sentinel-1 VV). Our approach shows excellent performance when transferring in different areas for the same crop type and rather promising results when transferring in a different area and crop type. Authors: Hyun-Woo Jo (Korea University); Alkiviadis Marios Koukos (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Woo-Kyun Lee (Korea University); Charalampos Kontoes (National Observatory of Athens)	Meta- and Transfer Learning Agriculture & Food Earth Observation & Monitoring Forests Computer Vision & Remote Sensing
NeurIPS 2022	Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds (Papers Track) Abstract and authors: (click to expand) Abstract: Pyrocumulonimbus (pyroCb) clouds are storm clouds generated by extreme wildfires. PyroCbs are associated with unpredictable, and therefore dangerous, wildfire spread. They can also inject smoke particles and trace gases into the upper troposphere and lower stratosphere, affecting the Earth's climate. As global temperatures increase, these previously rare events are becoming more common. Being able to predict which fires are likely to generate pyroCb is therefore key to climate adaptation in wildfire-prone areas. This paper introduces Pyrocast, a pipeline for pyroCb analysis and forecasting. The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented. The database brings together geostationary imagery and environmental data for over 148 pyroCb events across North America, Australia, and Russia between 2018 and 2022. Random Forests, Convolutional Neural Networks (CNNs), and CNNs pretrained with Auto-Encoders were tested to predict the generation of pyroCb for a given fire six hours in advance. The best model predicted pyroCb with an AUC of 0.90±0.04. Authors: Kenza Tazi (University of Cambridge); Emiliano Díaz Salas-Porras (University of Valencia); Ashwin Braude (Institut Pierre-Simon Laplace); Daniel Okoh (National Space Research and Development Agency); Kara D. Lamb (Columbia University); Duncan Watson-Parris (University of Oxford); Paula Harder (Fraunhofer ITWM); Nis Meinert (Pasteur Labs)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022	Evaluating Digital Tools for Sustainable Agriculture using Causal Inference (Papers Track) Abstract and authors: (click to expand) Abstract: In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop the causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently we estimate it using several methods on observational data. The results showed that a field sown according to our recommendations enjoyed a significant increase in yield 12% to 17%. Authors: Ilias Tsoumas (National Observatory of Athens); Georgios Giannarakis (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Alkiviadis Marios Koukos (National Observatory of Athens); Dimitra A Loka (Hellenic Agricultural Organization ELGO DIMITRA); Nikolaos S Bartsotas (National Observatory of Athens); Charalampos Kontoes (National Observatory of Athens); Ioannis N Athanasiadis (Wageningen University and Research)	Causal & Bayesian Methods Agriculture & Food Earth Observation & Monitoring Extreme Weather Societal Adaptation & Resilience
NeurIPS 2022	Generating physically-consistent high-resolution climate data with hard-constrained neural networks (Papers Track) Abstract and authors: (click to expand) Abstract: The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often can only make coarse resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using image super-resolution methods from computer vision. Despite achieving visually compelling results in some cases, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we developed a deep downscaling method that guarantees physical constraints are satisfied, by adding a renormalization layer at the end of the neural network. Furthermore, the constrained model also improves the performance according to standard metrics. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data. Authors: Paula Harder (Mila); Qidong Yang (New York University); Venkatesh Ramesh (Mila); Prasanna Sattigeri (IBM Research); Alex Hernandez-Garcia (Mila - Quebec AI Institute); Campbell D Watson (IBM Reserch); Daniela Szwarcman (IBM Research); David Rolnick (McGill University, Mila)	Climate Science & Modeling
NeurIPS 2022	Transformers for Fast Emulation of Atmospheric Chemistry Box Models (Papers Track) Abstract and authors: (click to expand) Abstract: When modeling atmospheric chemistry, concentrations are determined by numerically solving large systems of ordinary differential equations that represent a set of chemical reactions. These solvers can be very computationally intensive, particularly those with the thousands or tens of thousands of chemical species and reactions that make up the most accurate models. We demonstrate the application of a deep learning transformer architecture to emulate an atmospheric chemistry box model, and show that this attention-based model outperforms LSTM and autoencoder baselines while providing interpretable predictions that are more than 2 orders of magnitude faster than a numerical solver. This work is part of a larger study to replace the numerical solver in a 3D global chemical model with a machine learned emulator and achieve significant speedups for global climate simulations. Authors: Herbie Bradley (University of Cambridge); Nathan Luke Abraham (National Centre for Atmospheric Science, UK); Peer Nowack (Imperial College London); Doug McNeall (Met Office Hadley Centre, UK)	Climate Science & Modeling Hybrid Physical Models Time-series Analysis
NeurIPS 2022	Flood Prediction with Graph Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is increasing the frequency of flooding around the world. As a consequence, there is a growing demand for effective flood prediction. Machine learning is a promising alternative to hydrodynamic models for flood prediction. However, existing approaches focus on capturing either the spatial or temporal flood patterns using CNNs or RNNs, respectively. In this work, we propose FloodGNN, which is a graph neural network (GNN) for flood prediction. Compared to existing approaches, FloodGNN (i) employs a graph-based model (GNN); (ii) operates on both spatial and temporal dimensions; and (iii) processes the water flow velocities as vector features, instead of scalar features. Experiments show that FloodGNN achieves promising results, outperforming an RNN-based baseline. Authors: Arnold N Kazadi (Rice University); James Doss-Gollin (Rice University); Antonia Sebastian (UNC Chapel Hill); Arlei Silva (Rice University)	Extreme Weather
NeurIPS 2022	Neural Representation of the Stratospheric Ozone Layer (Papers Track) Abstract and authors: (click to expand) Abstract: In climate modeling, the stratospheric ozone layer is typically only considered in a highly simplified form due to computational constraints. For climate projections, it would be of advantage to include the mutual interactions between stratospheric ozone, temperature, and atmospheric dynamics to accurately represent radiative forcing. The overarching goal of our research is to replace the ozone chemistry in climate models with a machine-learned neural representation of the stratospheric ozone chemistry that allows for a particularly fast, but accurate and stable simulation. We created a benchmark data set from pairs of input and output variables that we stored from simulations of a chemistry and transport model. We analyzed several variants of multilayer perceptrons suitable for physical problems to learn a neural representation of a function that predicts 24-hour ozone tendencies based on input variables. We performed a comprehensive hyperparameter optimization of the multilayer perceptron using Bayesian search and Hyperband early stopping. We validated our model by implementing it in a chemistry and transport model and comparing computation time, accuracy, and stability with the full chemistry module. We found that our model had a computation time that was a factor of 700 faster than the full chemistry module. The accuracy of our model compares favorably to the full chemistry module within a two-year simulation run, also outperforms a previous polynomial approach for fast ozone chemistry, and reproduces seasonality well in both hemispheres. In conclusion, the neural representation of stratospheric ozone chemistry in simulation resulted in an ozone layer that showed a high accuracy, significant speed-up, and stability in a long-term simulation. Authors: Helge Mohn (Alfred Wegener Institute for Polar and Marine Research); Daniel Kreyling (Alfred Wegener Institute for Polar and Marine Research); Ingo Wohltmann (Alfred Wegener Institute for Polar and Marine Research); Ralph Lehmann (Alfred Wegener Institute for Polar and Marine Research); Peter Maaß (University of Bremen); Markus Rex (Alfred Wegener Institute for Polar and Marine Research)	Climate Science & Modeling
NeurIPS 2022	DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on mesh-gridded weather data without the utilization of raw climate observational data, the gold standard ground truth. In this work we develop a methodology to correct, remap, and fine-tune gridded uniform forecasts of FCN so it can be directly compared against observational ground truth, which is sparse and non-uniform in space and time. This is akin to bias-correction and post-processing of numerical weather prediction (NWP), a routine operation at meteorological and weather forecasting centers across the globe. The Adaptive Fourier Neural Operator (AFNO) architecture is used as the backbone to learn continuous representations of the atmosphere. The spatially and temporally non-uniform output is evaluated by the non-uniform discrete inverse Fourier transform (NUIDFT) given the output query locations. We call this network the Deep-Learning-Corrector-Remapper (DLCR). The improvement in DLCR’s performance against the gold standard ground truth over the baseline’s performance shows its potential to correct, remap, and fine-tune the mesh-gridded forecasts under the supervision of observations. Authors: Tao Ge (Washington University in St. Louis); Jaideep Pathak (NVIDIA Corporation); Akshay Subramaniam (NVIDIA); Karthik Kashinath (NVIDIA)	Climate Science & Modeling Extreme Weather
NeurIPS 2022	Industry-scale CO2 Flow Simulations with Model-Parallel Fourier Neural Operators (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon capture and storage (CCS) is one of the most promising technologies for reducing greenhouse gas emissions and relies on numerical reservoir simulations for identifying and monitoring CO2 storage sites. In many commercial settings however, numerical reservoir simulations are too computationally expensive for important downstream application such as optimization or uncertainty quantification. Deep learning-based surrogate models offer the possibility to solve PDEs many orders of magnitudes faster than conventional simulators, but they are difficult to scale to industrial-scale problem settings. Using model-parallel deep learning, we train the largest CO2 surrogate model to date on a 3D simulation grid with two million grid points. To train the 3D simulator, we generate a new training dataset based on a real-world CCS simulation benchmark. Once trained, each simulation with the network is five orders of magnitude faster than a numerical reservoir simulator and 4,500 times cheaper. This paves the way to applications that require thousands of (sequential) simulations, such as optimizing the location of CO2 injection wells to maximize storage capacity and minimize risk of leakage. Authors: Philipp A Witte (Microsoft); Russell Hewett (Microsoft); Ranveer Chandra (Microsoft Research)	Carbon Capture & Sequestration
NeurIPS 2022	Adaptive Bias Correction for Improved Subseasonal Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Subseasonal forecasting — predicting temperature and precipitation 2 to 6 weeks ahead — is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remains poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. To counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% and precipitation forecasting skill by 40-69% in the contiguous U.S. We couple these performance improvements with a practical workflow, based on Cohort Shapley, for explaining ABC skill gains and identifying higher-skill windows of opportunity based on specific climate conditions. Authors: Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Judah Cohen (AER); Miruna Oprescu (Cornell University); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research New England)	Climate Science & Modeling Extreme Weather
NeurIPS 2022	Nowformer : A Locally Enhanced Temporal Learner for Precipitation Nowcasting (Papers Track) Abstract and authors: (click to expand) Abstract: The precipitation video datasets have distinctive meteorological patterns where a mass of fluid moves in a particular direction across the entire frames, and each local area of the fluid has an individual life cycle from initiation to maturation to decay. This paper proposes a novel transformer-based model for precipitation nowcasting that can extract global and local dynamics within meteorological characteristics. The experimental results show our model achieves state-of-the-art performances on the precipitation nowcasting benchmark. Authors: Jinyoung Park (KAIST); Inyoung Lee (KAIST); Minseok Son (KAIST); Seungju Cho (KAIST); Changick Kim (KAIST)	Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2022	An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes (Papers Track) Abstract and authors: (click to expand) Abstract: Despite the importance of quantifying how the spatial patterns of extreme precipitation will change with warming, we lack tools to objectively analyze the storm-scale outputs of modern climate models. To address this gap, we develop an unsupervised machine learning framework to quantify how storm dynamics affect precipitation extremes and their changes without sacrificing spatial information. Over a wide range of precipitation quantiles, we find that the spatial patterns of extreme precipitation changes are dominated by spatial shifts in storm regimes rather than intrinsic changes in how these storm regimes produce precipitation. Authors: Griffin S Mooers (UC Irvine); Tom Beucler (University of Lausanne); Michael Pritchard (UCI); Stephan Mandt (University of California, Irivine)	Climate Science & Modeling Unsupervised & Semi-Supervised Learning
NeurIPS 2022	An Interpretable Model of Climate Change Using Correlative Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Determining changes in global temperature and precipitation that may indicate climate change is complicated by annual variations. One approach for finding potential climate change indicators is to train a model that predicts the year from annual means of global temperatures and precipitations. Such data is available from the CMIP6 ensemble of simulations. Here a two-hidden-layer neural network trained on this data successfully predicts the year. Differences among temperature and precipitation patterns for which the model predicts specific years reveal changes through time. To find these optimal patterns, a new way of interpreting what the neural network has learned is explored. Alopex, a stochastic correlative learning algorithm, is used to find optimal temperature and precipitation maps that best predict a given year. These maps are compared over multiple years to show how temperature and precipitations patterns indicative of each year change over time. Authors: Charles Anderson (Colorado State University); Jason Stock (Colorado State University)	Climate Science & Modeling Interpretable ML
NeurIPS 2022	Multimodal Wildland Fire Smoke Detection (Papers Track) Abstract and authors: (click to expand) Abstract: Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. Our results show that incorporating multimodal data in SmokeyNet improves performance in terms of both F1 and time-to-detection over the baseline with a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires. Authors: Mai Nguyen (University of California San Diego); Shreyas Anantha Ramaprasad (University of California San Diego); Jaspreet Kaur Bhamra (University of California San Diego); Siddhant Baldota (University of California San Diego); Garrison Cottrell (UC San Diego)	Disaster Management and Relief Computer Vision & Remote Sensing
NeurIPS 2022	Using uncertainty-aware machine learning models to study aerosol-cloud interactions (Papers Track) Abstract and authors: (click to expand) Abstract: Aerosol-cloud interactions (ACI) include various effects that result from aerosols entering a cloud, and affecting cloud properties. In general, an increase in aerosol concentration results in smaller droplet sizes which leads to larger, brighter, longer-lasting clouds that reflect more sunlight and cool the Earth. The strength of the effect is however heterogeneous, meaning it depends on the surrounding environment, making ACI one of the most uncertain effects in our current climate models. In our work, we use causal machine learning to estimate ACI from satellite observations by reframing the problem as a treatment (aerosol) and outcome (change in droplet radius). We predict the causal effect of aerosol on clouds with uncertainty bounds depending on the unknown factors that may be influencing the impact of aerosol. Of the three climate models evaluated, we find that only one plausibly recreates the trend, lending more credence to its estimate cooling due to ACI. Authors: Maëlys Solal (University of Oxford); Andrew Jesson (University of Oxford); Yarin Gal (University of Oxford); Alyson Douglas (University of Oxford)	Causal & Bayesian Methods Climate Science & Modeling Earth Observation & Monitoring Uncertainty Quantification & Robustness
NeurIPS 2022	Accessible Large-Scale Plant Pathology Recognition (Papers Track) Abstract and authors: (click to expand) Abstract: Plant diseases are costly and threaten agricultural production and food security worldwide. Climate change is increasing the frequency and severity of plant diseases and pests. Therefore, detection and early remediation can have a significant impact, especially in developing countries. However, AI solutions are yet far from being in production. The current process for plant disease diagnostic consists of manual identification and scoring by humans, which is time-consuming, low-supply, and expensive. Although computer vision models have shown promise for efficient and automated plant disease identification, there are limitations for real-world applications: a notable variation in visual symptoms of a single disease, different light and weather conditions, and the complexity of the models. In this work, we study the performance of efficient classification models and training "tricks" to solve this problem. Our analysis represents a plausible solution for these ecological disasters and might help to assist producers worldwide. More information available at: https://github.com/mv-lab/mlplants Authors: Marcos V. Conde (University of Würzburg); Dmitry Gordeev (H2O.ai)	Computer Vision & Remote Sensing Disaster Management and Relief Forests Health Active Learning
NeurIPS 2022	Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano Particles in a contaminated aquifer (Papers Track) Abstract and authors: (click to expand) Abstract: Numerous polluted groundwater sites across the globe require an active remediation strategy for the restoration of natural environmental conditions and local ecosystem. The Engineered Nanoparticles (ENPs) has emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. While the performance of these ENPs has been highly promising on the laboratory scale, their application in a real field case conditions is still limited. The optimized injection of the ENPs in the contaminated aquifer and its subsequent monitoring are hindered by the complex transport and retention mechanisms of ENPs. Therefore, a predictive tool for understanding the transport and retention behavior of ENPs becomes highly important. The existing tools in the literature are dominated with numerical simulators, which have limited flexibility and accuracy in the presence of sparse dataset. In this work, a dynamic weights enabled Physics-Informed Neural network (dw-PINN) framework is applied to model the nano-particle´s behavior within an aquifer. The result from the forward model demonstrates the effective capability of dw-PINN in accurately predicting the ENPs mobility. The model verification step shows that the mean squared error of the predicted ENPs concentration using dw-PINN converges to a minimum value of 1.3e-5. In the subsequent step, the result from the inverse model estimates the governing parameters of ENPs mobility with reasonable accuracy. The research work demonstrates the tool´s capability in providing predictive insights for the development of an efficient groundwater remediation strategy. Authors: Shikhar Nilabh (Amphos21)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2022	Calibration of Large Neural Weather Models (Papers Track) Abstract and authors: (click to expand) Abstract: Uncertainty quantification of weather forecasts is a necessity for reliably planning for and responding to extreme weather events in a warming world. This motivates the need for well-calibrated ensembles in probabilistic weather forecasting. We present initial results for the calibration of large-scale deep neural weather models for data-driven probabilistic weather forecasting. By explicitly accounting for uncertainties about the forecast's initial condition and model parameters, we generate ensemble forecasts that show promising results on standard diagnostics for probabilistic forecasts. Specifically, we are approaching the Integrated Forecasting System (IFS), the gold standard on probabilistic weather forecasting, on: (i) the spread-error agreement; and (ii) the Continuous Ranked Probability Score (CRPS). Our approach scales to state-of-the-art data-driven weather models, enabling cheap post-hoc calibration of pretrained models with tens of millions of parameters and paving the way towards the next generation of well-calibrated data-driven weather models. Authors: Andre Graubner (Nvidia); Kamyar Kamyar Azizzadenesheli (Nvidia); Jaideep Pathak (NVIDIA Corporation); Morteza Mardani (Nvidia); Mike Pritchard (Nvidia); Karthik Kashinath (Nvidia); Anima Anandkumar (NVIDIA/Caltech)	Uncertainty Quantification & Robustness Climate Science & Modeling
NeurIPS 2022	Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs (Papers Track) Abstract and authors: (click to expand) Abstract: Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant for socio-economic functioning, which if absent can lead to humanitarian disasters. Vegetation status depends on weather and environmental conditions that modulate complex ecological processes taking place at several timescales. Interactions between vegetation and different environmental drivers express responses at instantaneous but also time-lagged effects, often showing an emerging spatial context at landscape and regional scales. We formulate the land surface forecasting task as a strongly guided video prediction task where the objective is to forecast the vegetation developing at very fine resolution using topography and weather variables to guide the prediction. We use a Convolutional LSTM (ConvLSTM) architecture to address this task and predict changes in the vegetation state in Africa using Sentinel-2 satellite NDVI, having ERA5 weather reanalysis, SMAP satellite measurements, and topography (DEM of SRTMv4.1) as variables to guide the prediction. Ours results highlight how ConvLSTM models can not only forecast the seasonal evolution of NDVI at high resolution, but also the differential impacts of weather anomalies over the baselines. The model is able to predict different vegetation types, even those with very high NDVI variability during target length. Authors: Claire Robin (Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany); Christian Requena-Mesa (Computer Vision Group, Friedrich Schiller University Jena; DLR Institute of Data Science, Jena; Max Planck Institute for Biogeochemistry, Jena); Vitus Benson (Max-Planck-Institute for Biogeochemistry); Jeran Poehls (Max-Planck-Institute for Biogeochemistry); Lazaro Alonzo (Max-Planck-Institute for Biogeochemistry Max-Planck-Institute for Biogeochemistry); Nuno Carvalhais (Max-Planck-Institute for Biogeochemistry); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena)	Computer Vision & Remote Sensing Agriculture & Food Ecosystems & Biodiversity Extreme Weather Time-series Analysis
NeurIPS 2022	Generative Modeling of High-resolution Global Precipitation Forecasts (Papers Track) Abstract and authors: (click to expand) Abstract: Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change. Making accurate high-resolution precipitation forecasts using traditional physical models remains a major challenge in operational weather forecasting as they incur substantial computational costs and struggle to achieve sufficient forecast skill. Recently, deep-learning-based models have shown great promise in closing the gap with numerical weather prediction (NWP) models in terms of precipitation forecast skill, opening up exciting new avenues for precipitation modeling. However, it is challenging for these deep learning models to fully resolve the fine-scale structures of precipitation phenomena and adequately characterize the extremes of the long-tailed precipitation distribution. In this work, we present several improvements to the architecture and training process of a current state-of-the art deep learning precipitation model (FourCastNet) using a novel generative adversarial network (GAN) to better capture fine scales and extremes. Our improvements achieve superior performance in capturing the extreme percentiles of global precipitation, while comparable to state-of-the-art NWP models in terms of forecast skill at 1--2 day lead times. Together, these improvements set a new state-of-the-art in global precipitation forecasting. Authors: James Duncan (University of California, Berkeley); Peter Harrington (Lawrence Berkeley National Laboratory (Berkeley Lab)); Shashank Subramanian (Lawrence Berkeley National Laboratory)	Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Generative Modeling
NeurIPS 2022	Learning Surrogates for Diverse Emission Models (Papers Track) Abstract and authors: (click to expand) Abstract: Transportation plays a major role in global CO2 emission levels, a factor that directly connects with climate change. Roadway interventions that reduce CO2 emission levels have thus become a timely requirement. An integral need in assessing the impact of such roadway interventions is access to industry-standard programmatic and instantaneous emission models with various emission conditions such as fuel types, vehicle types, cities of interest, etc. However, currently, there is a lack of well-calibrated emission models with all these properties. Addressing these limitations, this paper presents 1100 programmatic and instantaneous vehicular CO2 emission models with varying fuel types, vehicle types, road grades, vehicle ages, and cities of interest. We hope the presented emission models will facilitate future research in tackling transportation-related climate impact. The released version of the emission models can be found here. Authors: Edgar Ramirez Sanchez (MIT); Catherine H Tang (Massachusetts Institute of Technology); Vindula Jayawardana (MIT); Cathy Wu (MIT)	Transportation Cities & Urban Planning Data Mining
NeurIPS 2022	Continual VQA for Disaster Response Systems (Papers Track) Abstract and authors: (click to expand) Abstract: Visual Question Answering (VQA) is a multi-modal task that involves answering questions from an input image, semantically understanding the contents of the image and answering it in natural language. Using VQA for disaster management is an important line of research due to the scope of problems that are answered by the VQA system. However, the main challenge is the delay caused by the generation of labels in the assessment of the affected areas. To tackle this, we deployed pre-trained CLIP model, which is trained on visual-image pairs. however, we empirically see that the model has poor zero-shot performance. Thus, we instead use pre-trained embeddings of text and image from this model for our supervised training and surpass previous state-of-the-art results on the FloodNet dataset. We expand this to a continual setting, which is a more real-life scenario. We tackle the problem of catastrophic forgetting using various experience replay methods. Authors: Aditya Kane (Pune Institute of Computer Technology); V Manushree (Manipal Institute Of Technology); Sahil S Khose (Georgia Institute of Technology)	Disaster Management and Relief Active Learning
NeurIPS 2022	Performance evaluation of deep segmentation models on Landsat-8 imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through the cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activities. Avoiding contrails and adjusting flight routes could be an inexpensive and effective way to reduce their impact. An accurate, automated, and reliable detection algorithm is required to develop and evaluate contrail avoidance strategies. Advancement in contrail detection has been severely limited due to several factors, primarily due to a lack of quality-labelled data. Recently, McCloskey et al. proposed a large human-labelled Landsat-8. Each contrail is carefully labelled with various inputs in various scenes of Landsat-8 satellite imagery. In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones. This work is the first to apply state-of-the-art segmentation techniques to detect contrails in low-orbit satellite imagery. Our work can also be used as an open benchmark for contrail segmentation. Authors: Akshat Bhandari (Manipal Institute of Technology, Manipal); Pratinav Seth (Manipal Institute of Technology); Sriya Rallabandi (Manipal Institute of Technology); Aditya Kasliwal (Manipal Institute of Technology); Sanchit Singhal (Manipal Institute of Technology)	Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022	Synthesis of Realistic Load Data: Adversarial Networks for Learning and Generating Residential Load Patterns (Papers Track) Abstract and authors: (click to expand) Abstract: Responsible energy consumption plays a key role in reducing carbon footprint and CO2 emissions to tackle climate change. A better understanding of the residential consumption behavior using smart meter data is at the heart of the mission, which can inform residential demand flexibility, appliance scheduling, and home energy management. However, access to high-quality residential load data is still limited due to the cost-intensive data collection process and privacy concerns of data shar- ing. In this paper, we develop a Generative Adversarial Network (GAN)-based method to model the complex and diverse residential load patterns and generate synthetic yet realistic load data. We adopt a generation-focused weight selection method to select model weights to address the mode collapse problem and generate diverse load patterns. We evaluate our method using real-world data and demon- strate that it outperforms three representative state-of-the-art benchmark models in better preserving the sequence level temporal dependencies and aggregated level distributions of load patterns. Authors: Xinyu Liang (Monash University); Hao Wang (Monash University)	Power & Energy Generative Modeling
NeurIPS 2022	Guided Transformer Network for Detecting Methane Emissions in Sentinel-2 Satellite Imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: Methane (CH4) is the chief contributor to global climate change and its mitigation is targeted by the EU, US and jurisdictions worldwide [2]. Recent studies have shown that imagery from the multi-spectral instrument on Sentinel-2 satellites is capable of detecting and estimating large methane emissions. However, most of the current methods rely on temporal relations between a ratio of shortwave-infrared spectra and assume relatively constant ground conditions, and availability of ground information on when there was no methane emission on site. To address such limitations we propose a guided query-based transformer neural network architecture, that will detect and quantify methane emissions without dependence on temporal information. The guided query aspect of our architecture is driven by a Sentinel Enhanced Matched Filter (SEMF) approach, also discussed in this paper. Our network uses all 12 spectral channels of Sentinel-2 imagery to estimate ground terrain and detect methane emissions. No dependence on temporal data makes it more robust to changing ground and terrain conditions and more computationally efficient as it reduces the need to process historical time-series imagery to compute a single date emissions analysis. Authors: Satish Kumar (University of California, Santa Barbara); William Kingwill (Orbio Earth); Rozanne Mouton (Orbio Earth); Wojciech Adamczyk (ETH, Zurich); Robert Huppertz (Orbio Earth); Evan D Sherwin (Stanford University, Energy and Resources Engineering)	Carbon Capture & Sequestration Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022	Identification of medical devices using machine learning on distribution feeder data for informing power outage response (Proposals Track) Abstract and authors: (click to expand) Abstract: Power outages caused by extreme weather events due to climate change have doubled in the United States in the last two decades. Outages pose severe health risks to over 4.4 million individuals dependent on in-home medical devices. Data on the number of such individuals residing in a given area is limited. This study proposes a load disaggregation model to predict the number of medical devices behind an electric distribution feeder. This data can be used to inform planning and response to power outages and other emergencies. The proposed solution serves as a measure for climate change adaptation. Authors: Paraskevi Kourtza (University of Edinburgh); Maitreyee Marathe (University of Wisconsin-Madison); Anuj Shetty (Stanford University); Diego Kiedanski (Yale University)	Power & Energy Disaster Management and Relief Health Time-series Analysis
NeurIPS 2022	Analyzing the global energy discourse with machine learning (Proposals Track) Abstract and authors: (click to expand) Abstract: To transform our economy towards net-zero emissions, industrial development of clean energy technologies (CETs) to replace fossil energy technologies (FETs) is crucial. Although the media has great power in influencing consumer behavior and decision making in business and politics, its role in the energy transformation is still underexplored. In this paper, we analyze the global energy discourse via machine learning. For this, we collect a large-scale dataset with ~5 million news articles from seven of the world’s major CO2 emitting countries, covering eight CETs and four FETs. Using machine learning, we then analyze the content of news articles on a highly granular level and along several dimensions, namely relevance (for the energy discourse), context (e.g., costs, regulation, investment), and connotations (e.g., high/increasing vs. low/decreasing costs). By linking empirical discourse patterns to investment and deployment data of CETs and FETs, this study advances the current understanding about the role of the media in the energy transformation. Thereby, it enables businesses, investors, and policy makers to respond more effectively to sensitive topics in the media discourse and leverage windows of opportunity for scaling CETs. Authors: Malte Toetzke (ETH Zurich); Benedict Probst (ETH Zurich); Yasin Tatar (ETH Zurich); Stefan Feuerriegel (LMU Munich); Volker Hoffmann (ETH Zurich)	Public Policy Behavioral and Social Science Natural Language Processing
NeurIPS 2022	Deep-S2SWind: A data-driven approach for improving Sub-seasonal wind predictions (Proposals Track) Abstract and authors: (click to expand) Abstract: A major transformation to mitigate climate change implies a rapid decarbonisation of the energy system and thus, increasing the use of renewable energy sources, such as wind power. However, renewable resources are strongly dependent on local and large-scale weather conditions, which might be influenced by climate change. Weather-related risk assessments are essential for the energy sector, in particular, for power system management decisions, for which forecasts of climatic conditions from several weeks to months (i.e. sub-seasonal scales) are of key importance. Here, we propose a data-driven approach to predict wind speed at longer lead-times that can benefit the energy sector. The main goal of this study is to assess the potential of machine learning algorithms to predict periods of low wind speed conditions that have a strong impact on the energy sector. Authors: Noelia Otero Felipe (University of Bern); Pascal Horton (University of Bern)	Climate Science & Modeling Earth Observation & Monitoring Interpretable ML Time-series Analysis
NeurIPS 2022	Towards Low Cost Automated Monitoring of Life Below Water to De-risk Ocean-Based Carbon Dioxide Removal and Clean Power (Proposals Track) Abstract and authors: (click to expand) Abstract: Oceans will play a crucial role in our efforts to combat the growing climate emergency. Researchers have proposed several strategies to harness greener energy through oceans and use oceans as carbon sinks. However, the risks these strategies might pose to the ocean and marine ecosystem are not well understood. It is imperative that we quickly develop a range of tools to monitor ocean processes and marine ecosystems alongside the technology to deploy these solutions on a large scale into the oceans. Large arrays of inexpensive cameras placed deep underwater coupled with machine learning pipelines to automatically detect, classify, count and estimate fish populations have the potential to continuously monitor marine ecosystems and help study the impacts of these solutions on the ocean. In this proposal, we discuss the challenges presented by a dark artificially lit underwater video dataset captured 500m below the surface, propose potential solutions to address these challenges, and present preliminary results from detecting and classifying 6 species of fish in deep underwater camera data. Authors: Kameswari Devi Ayyagari (Dalhousie University); Christopher Whidden (Dalhousie University); Corey Morris (Department of Fisheries and Oceans); Joshua Barnes (National Research Council Canada)	Oceans & Marine Systems Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2022	Towards the Automatic Analysis of Ceilometer Backscattering Profiles using Unsupervised Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Ceilometers use a laser beam to capture certain phenomena in the atmosphere like clouds, precipitation, or aerosol layers. These measurements can be visualized in so-called quick looks that at the moment are mostly analyzed manually by meteorology experts. In this work, we illustrate the path towards the automatic analysis of quick looks by using a hybrid approach combining an image segmentation algorithm with unsupervised representation learning and clustering. We present a first proof of concept and give an outlook on possible future work. Authors: Michael Dammann (HAW Hamburg); Ina Mattis (Deutscher Wetterdienst); Michael Neitzke (HAW Hamburg); Ralf Möller (University of Lübeck)	Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Unsupervised & Semi-Supervised Learning
NeurIPS 2022	Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics (Proposals Track) Abstract and authors: (click to expand) Abstract: Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact candidate for replacing LGVs, with experts estimating over half of urban van deliveries being replaceable by cargo bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. By modelling the relative delivery performance of different vehicle types across urban micro-regions, machine learning can help operators evaluate the business and environmental impact of adding cargo-bikes to their fleets. In this paper, we introduce two datasets, and present initial progress in modelling urban delivery service time (e.g. cruising for parking, unloading, walking). Using Uber’s H3 index to divide the cities into hexagonal cells, and aggregating OpenStreetMap tags for each cell, we show that urban context is a critical predictor of delivery performance. Authors: Max C Schrader (University of Alabama); Navish Kumar (IIT Kharagpur); Nicolas Collignon (University of Edinburgh); Maria S Astefanoaei (IT University of Copenhagen); Esben Sørig (Kale Collective); Soonmyeong Yoon (Kale Collective); Kai Xu (University of Edinburgh); Akash Srivastava (MIT-IBM)	Cities & Urban Planning Transportation Data Mining Unsupervised & Semi-Supervised Learning
NeurIPS 2022	An Inversion Algorithm of Ice Thickness and InSAR Data for the State of Friction at the Base of the Greenland Ice Sheet (Proposals Track) Abstract and authors: (click to expand) Abstract: With the advent of climate change and global warming, the Greenland Ice Sheet (GrIS) has been melting at an alarming rate, losing over 215 Gt per yr, and accounting for 10% of mean global sea level rise since the 1990s. It is imperative to understand what dynamics are causing ice loss and influencing ice flow in order to successfully project mass changes of ice sheets and associated sea level rise. This work applies machine learning, ice thickness data, and horizontal ice velocity measurements from satellite radar data to quantify the magnitudes and distributions of the basal traction forces that are holding the GrIS back from flowing into the ocean. Our approach uses a hybrid model: InSAR velocity data trains a linear regression model, and these model coefficients are fed into a geophysical algorithm to estimate basal tractions that capture relationships between the ice motion and physical variables. Results indicate promising model performance and reveal significant presence of large basal traction forces around the coastline of the GrIS. Authors: Aryan Jain (Amador Valley High School); Jeonghyeop Kim (Stony Brook University); William Holt (Stony Brook University)	Climate Science & Modeling Earth Observation & Monitoring Hybrid Physical Models
NeurIPS 2022	Deep learning-based bias adjustment of decadal climate predictions (Proposals Track) Abstract and authors: (click to expand) Abstract: Decadal climate predictions are key to inform adaptation strategies in a warming climate. Coupled climate models used for decadal predictions are, however, imperfect representations of the climate system leading to forecast biases. Biases can also result from a poor model initialization that, when combined with forecast drift, can produce errors depending non-linearly on lead time. We propose a deep learning-based bias correction approach for the post-processing of gridded forecasts to enhance the accuracy of decadal predictions. Authors: Reinel Sospedra-Alfonso (Environment and Climate Change Canada); Johannes Exenberger (Graz University of Technology); Marie C McGraw (Cooperative Institute for Research in the Atmosphere | CIRA); Trung Kien Dang (National University of Singapore)	Climate Science & Modeling
NeurIPS 2022	Surrogate Modeling for Methane Dispersion Simulations Using Fourier Neural Operator (Proposals Track) Abstract and authors: (click to expand) Abstract: Methane leak detection and remediation are critical for tackling climate change, where methane dispersion simulations play an important role in emission source attribution. As 3D modeling of methane dispersion is often costly and time-consuming, we train a deep-learning-based surrogate model using the Fourier Neural Operator to learn the PDE solver in our study. Our preliminary result shows that our surrogate modeling provides a fast, accurate and cost-effective solution to methane dispersion simulations, thus reducing the cycle time of methane leak detection. Authors: Qie Zhang (Microsoft); Mirco Milletari (Microsoft); Yagna Deepika Oruganti (Microsoft); Philipp A Witte (Microsoft)	Climate Science & Modeling Earth Observation & Monitoring
NeurIPS 2022	Detecting Floods from Cloudy Scenes: A Fusion Approach Using Sentinel-1 and Sentinel-2 Imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: As the result of climate change, extreme flood events are becoming more frequent. To better respond to such disasters, and to test and calibrate flood models, we need accurate real-world data on flooding extent. Detection of floods from remote sensed imagery suffers from a widespread problem: clouds block flood scenes in images, leading to degraded and fragmented flood datasets. To address this challenge, we propose a workflow based on U-Net, and a dataset that detects flood in cloud-prone areas by fusing information from the Sentinel-1 and Sentinel-2 satellites. The expected result will be a reliable and detailed catalogue of flood extents and how they change through time, allowing us to better understand flooding in different morphological settings and climates. Authors: Qiuyang Chen (University of Edinburgh); Xenofon Karagiannis (Earth-i Ltd.); Simon M. Mudd (University of Edinburgh)	Computer Vision & Remote Sensing Disaster Management and Relief Extreme Weather Time-series Analysis
NeurIPS 2022	Urban Heat Island Detection and Causal Inference Using Convolutional Neural Networks (Proposals Track) Abstract and authors: (click to expand) Abstract: Compared to rural areas, urban areas experience higher temperatures for longer periods of time because of the urban heat island (UHI) effect. This increased heat stress leads to greater mortality, increased energy demand, regional changes to precipitation patterns, and increased air pollution. Urban developers can minimize the UHI effect by incorporating features that promote air flow and heat dispersion (e.g., increasing green space). However, understanding which urban features to implement is complex, as local meteorology strongly dictates how the environment responds to changes in urban form. In this proposal we describe a methodology for estimating the causal relationship between changes in urban form and changes in the UHI effect. Changes in urban form and temperature changes are measured using convolutional neural networks, and a causal inference matching approach is proposed to estimate causal relationships. The success of this methodology will enable urban developers to implement city-specific interventions to mitigate the warming planet's impact on cities. Authors: Zachary D Calhoun (Duke University); Ziyang Jiang (Duke University); Mike Bergin (Duke University); David Carlson (Duke University)	Cities & Urban Planning Buildings Climate Justice Climate Science & Modeling Disaster Management and Relief Extreme Weather Health Causal & Bayesian Methods Computer Vision & Remote Sensing Time-series Analysis
NeurIPS 2022	Forecasting Global Drought Severity and Duration Using Deep Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Drought detection and prediction is challenging due to the slow onset of the event and varying degrees of dependence on numerous physical and socio-economic factors that differentiate droughts from other natural disasters. In this work, we propose DeepXD (Deep learning for Droughts), a deep learning model with 26 physics-informed input features for SPI (Standardised Precipitation Index) forecasting to identify and classify droughts using monthly oceanic indices, global meteorological and vegetation data, location (latitude, longitude) and land cover for the years 1982 to 2018. In our work, we propose extracting features by considering the atmosphere and land moisture and energy budgets and forecasting global droughts on a seasonal and an annual scale at 1, 3, 6, 9, 12 and 24 months lead times. SPI helps us to identify the severity and the duration of the drought to classify them as meteorological, agricultural and hydrological. Authors: Akanksha Ahuja (NOA); Xin Rong Chua (Centre for Climate Research Singapore)	Agriculture & Food Climate Science & Modeling Disaster Management and Relief Earth Observation & Monitoring Extreme Weather Societal Adaptation & Resilience Data Mining
NeurIPS 2022	ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Restoring ecosystems and reducing deforestation are necessary tools to mitigate the anthropogenic climate crisis. Current measurements of forest carbon stock can be inaccurate, in particular for underrepresented and small-scale forests in the Global South, hindering transparency and accountability in the Monitoring, Reporting, and Verification (MRV) of these ecosystems. There is thus need for high quality datasets to properly validate ML-based solutions. To this end, we present ForestBench, which aims to collect and curate geographically-balanced gold-standard datasets of small-scale forest plots in the Global South, by collecting ground-level measurements and visual drone imagery of individual trees. These equitable validation datasets for ML-based MRV of nature-based solutions shall enable assessing the progress of ML models for estimating above-ground biomass, ground cover, and tree species diversity. Authors: Lucas Czech (Carnegie Institution for Science); Björn Lütjens (MIT); David Dao (ETH Zurich)	Earth Observation & Monitoring Carbon Capture & Sequestration Climate Finance & Economics Climate Justice Ecosystems & Biodiversity Forests Local and Indigenous Knowledge Systems Public Policy Computer Vision & Remote Sensing Land Use
NeurIPS 2022	Estimating Heating Loads in Alaska using Remote Sensing and Machine Learning Methods (Proposals Track) Abstract and authors: (click to expand) Abstract: Alaska and the larger Arctic region are in much greater need of decarbonization than the rest of the globe as a result of the accelerated consequences of climate change over the past ten years. Heating for homes and businesses accounts for over 75% of the energy used in the Arctic region. However, the lack of thorough and precise heating load estimations in these regions poses a significant obstacle to the transition to renewable energy. In order to accurately measure the massive heating demands in Alaska, this research pioneers a geospatial-first methodology that integrates remote sensing and machine learning techniques. Building characteristics such as height, size, year of construction, thawing degree days, and freezing degree days are extracted using open-source geospatial information in Google Earth Engine (GEE). These variables coupled with heating load forecasts from the AK Warm simulation program are used to train models that forecast heating loads on Alaska’s Railbelt utility grid. Our research greatly advances geospatial capability in this area and considerably informs the decarbonization activities currently in progress in Alaska. Authors: Madelyn Gaumer (University of Washington); Nick Bolten (Paul G. Allen School of Computer Science and Engineering, University of Washington); Vidisha Chowdhury (Heinz College of Information Systems and Public Policy, Carnegie Mellon University); Philippe Schicker (Heinz College of Information Systems and Public Policy, Carnegie Mellon University); Shamsi Soltani (Department of Epidemiology and Population Health, Stanford University School of Medicine); Erin D Trochim (University of Alaska Fairbanks)	Buildings Cities & Urban Planning Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022	Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times (Proposals Track) Abstract and authors: (click to expand) Abstract: There are many benefits from the accurate forecasting of Arctic sea ice, however existing models struggle to reliably predict sea ice concentration at long lead times. Many numerical models exist but can be sensitive to initial conditions, and while recent deep learning-based methods improve overall robustness, they either do not utilize temporal trends or rely on architectures that are not performant at learning long-term sequential dependencies. We propose a method of forecasting sea ice concentration using neural circuit policies, a form of continuous time recurrent neural architecture, which improve the learning of long-term sequential dependencies compared to existing techniques and offer the added benefits of adaptability to irregular sequence intervals and high interpretability. Authors: Matthew Beveridge (Independent Researcher); Lucas Pereira (ITI, LARSyS, Técnico Lisboa)	Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring Oceans & Marine Systems Interpretable ML Time-series Analysis Uncertainty Quantification & Robustness
NeurIPS 2022	CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate change is threatening human health in unprecedented orders and many ways. These threats are expected to grow unless effective and evidence-based policies are developed and acted upon to minimize or eliminate them. Attaining such a task requires the highest degree of the flow of knowledge from science into policy. The multidisciplinary, location-specific, and vastness of published science makes it challenging to keep track of novel work in this area, as well as making the traditional knowledge synthesis methods inefficient in infusing science into policy. To this end, we consider developing multiple domain-specific language models (LMs) with different variations from Climate- and Health-related information, which can serve as a foundational step toward capturing available knowledge to enable solving different tasks, such as detecting similarities between climate- and health-related concepts, fact-checking, relation extraction, evidence of health effects to policy text generation, and more. To our knowledge, this is the first work that proposes developing multiple domain-specific language models for the considered domains. We will make the developed models, resources, and codebase available for the researchers. Authors: Babak Jalalzadeh Fard (University of Nebraska Medical Center); Sadid A. Hasan (Microsoft); Jesse E. Bell (University of Nebraska Medical Center)	Health Natural Language Processing
NeurIPS 2022	Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid (Proposals Track) Abstract and authors: (click to expand) Abstract: This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting distributed energy resources (DER) such as renewables, energy storage, and loads in modern, low-carbon power grids. This will be achieved by (i) leveraging recently developed extensions of FL such as hierarchical and iterative clustering to improve performance with non-IID data, (ii) experimenting with different types of FL global models well-suited to time-series data, and (iii) incorporating domain-specific knowledge from power systems to build more general FL frameworks and architectures that can be applied to diverse types of DERs beyond just load forecasting, and with heterogeneous clients. Authors: Vineet Jagadeesan Nair (MIT)	Power & Energy Buildings Time-series Analysis
NeurIPS 2022	Personalizing Sustainable Agriculture with Causal Machine Learning (Proposals Track) Best Paper: Proposals Abstract and authors: (click to expand) Abstract: To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The "personalization" of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for the efficient uplift of green metrics, and an integral development in imminent policies. Here, we formulate personalized sustainable agriculture as a Conditional Average Treatment Effect estimation task and use Causal Machine Learning for tackling it. Leveraging climate data, land use information and employing Double Machine Learning, we estimate the heterogeneous effect of sustainable practices on the field-level Soil Organic Carbon content in Lithuania. We thus provide a data-driven perspective for targeting sustainable practices and effectively expanding the global carbon sink. Authors: Georgios Giannarakis (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Roxanne Suzette Lorilla (National Observatory of Athens); Charalampos Kontoes (National Observatory of Athens)	Causal & Bayesian Methods Agriculture & Food Carbon Capture & Sequestration Earth Observation & Monitoring Ecosystems & Biodiversity Public Policy Societal Adaptation & Resilience Data Mining
NeurIPS 2022	Disaster Risk Monitoring Using Satellite Imagery (Tutorials Track) Abstract and authors: (click to expand) Abstract: Natural disasters such as flood, wildfire, drought, and severe storms wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems, and economies. Unfortunately, flooding events are on the rise due to climate change and sea level rise. The ability to detect and quantify them can help us minimize their adverse impacts on the economy and human lives. Using satellites to study flood is advantageous since physical access to flooded areas is limited and deploying instruments in potential flood zones can be dangerous. We are proposing a hands-on tutorial to highlight the use of satellite imagery and computer vision to study natural disasters. Specifically, we aim to demonstrate the development and deployment of a flood detection model using Sentinel-1 satellite data. The tutorial will cover relevant fundamental concepts as well as the full development workflow of a deep learning-based application. We will include important considerations such as common pitfalls, data scarcity, augmentation, transfer learning, fine-tuning, and details of each step in the workflow. Importantly, the tutorial will also include a case study on how the application was used by authorities in response to a flood event. We believe this tutorial will enable machine learning practitioners of all levels to develop new technologies that tackle the risks posed by climate change. We expect to deliver the below learning outcomes: • Develop various deep learning-based computer vision solutions using hardware-accelerated open-source tools that are optimized for real-time deployment • Create an optimized pipeline for the machine learning development workflow • Understand different performance metrics for model evaluation that are relevant for real world datasets and data imbalances • Understand the public sector’s efforts to support climate action initiatives and point out where the audience can contribute Authors: Kevin Lee (NVIDIA); Siddha Ganju (NVIDIA); Edoardo Nemni (UNOSAT)	Disaster Management and Relief Climate Science & Modeling Earth Observation & Monitoring Extreme Weather Active Learning Computer Vision & Remote Sensing Data Mining Meta- and Transfer Learning
NeurIPS 2022	Machine Learning for Predicting Climate Extremes (Tutorials Track) Abstract and authors: (click to expand) Abstract: Climate change has led to a rapid increase in the occurrence of extreme weather events globally, including floods, droughts, and wildfires. In the longer term, some regions will experience aridification while others will risk sinking due to rising sea levels. Typically, such predictions are done via weather and climate models that simulate the physical interactions between the atmospheric, oceanic, and land surface processes that operate at different scales. Due to the inherent complexity, these climate models can be inaccurate or computationally expensive to run, especially for detecting climate extremes at high spatiotemporal resolutions. In this tutorial, we aim to introduce the participants to machine learning approaches for addressing two fundamental challenges. We will walk the participants through a hands-on tutorial for predicting climate extremes relating to temperature and precipitation in 2 setups: (1) temporal forecasting: the goal is to predict climate variables into the future (both direct single step approaches and iterative approaches that roll out the model for several timesteps), and (2) spatial downscaling: the goal is to learn a mapping that transforms low-resolution outputs of climate models into high-resolution regional forecasts. Through introductory presentations and colab notebooks, we aim to expose the participants to (a) APIs for accessing and navigating popular repositories that host global climate data, such as the Copernicus data store, (b) identifying relevant datasets, including auxiliary data (e.g., other climate variables such as geopotential), (c) scripts for downloading and preprocessing relevant datasets, (d) algorithms for training machine learning models, (d) metrics for evaluating model performance, and (e) visualization tools for both the dataset and predicted outputs. The coding notebooks will be in Python. No prior knowledge of climate science is required. Authors: Hritik Bansal (UCLA); Shashank Goel (University of California Los Angeles); Tung Nguyen (University of California, Los Angeles); Aditya Grover (UCLA)	Climate Science & Modeling Earth Observation & Monitoring Extreme Weather Data Mining Hybrid Physical Models
NeurIPS 2022	FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator (Tutorials Track) Abstract and authors: (click to expand) Abstract: Accurate, reliable, and efficient means of forecasting global weather patterns are of paramount importance to our ability to mitigate and adapt to climate change. Currently, real-time weather forecasting requires repeated numerical simulation and data assimilation cycles on dedicated supercomputers, which restricts the ability to make reliable, high-resolution forecasts to a handful of organizations. However, recent advances in deep learning, specifically the FourCastNet model, have shown that data-driven approaches can forecast important atmospheric variables with excellent skill and comparable accuracy to standard numerical methods, but at orders-of-magnitude lower computational and energy cost during inference, enabling larger ensembles for better probabilistic forecasts. In this tutorial, we demonstrate various applications of FourCastNet for high-resolution global weather forecasting, with examples including real-time forecasts, uncertainty quantification for extreme events, and adaptation to specific variables or localized regions of interest. The tutorial will provide examples that will demonstrate the general workflow for formatting and working with global atmospheric data, running autoregressive inference to obtain daily global forecasts, saving/visualizing model predictions of atmospheric events such as hurricanes and atmospheric rivers, and computing quantitative evaluation metrics for weather models. The exercises will primarily use PyTorch and do not require detailed understanding of the climate and weather system. With this tutorial, we hope to equip attendees with basic knowledge about building deep learning-based weather model surrogates and obtaining forecasts of crucial atmospheric variables using these models. Authors: Jaideep Pathak (NVIDIA Corporation); Shashank Subramanian (Lawrence Berkeley National Laboratory); Peter Harrington (Lawrence Berkeley National Laboratory (Berkeley Lab)); Thorsten Kurth (Nvidia); Andre Graubner (Nvidia); Morteza Mardani (NVIDIA Corporation); David M. Hall (NVIDIA); Karthik Kashinath (Lawrence Berkeley National Laboratory); Anima Anandkumar (NVIDIA/Caltech)	Climate Science & Modeling Extreme Weather Uncertainty Quantification & Robustness
NeurIPS 2022	Automating the creation of LULC datasets for semantic segmentation (Tutorials Track) Abstract and authors: (click to expand) Abstract: High resolution and accurate Land Use and Land Cover mapping (LULC) datasets are increasingly important and can be widely used in monitoring climate change impacts in agriculture, deforestation, and the carbon cycle. These datasets represent physical classifications of land types and spatial information over the surface of the Earth. These LULC datasets can be leveraged in a plethora of research topics and industries to mitigate and adapt to environmental changes. High resolution urban mappings can be used to better monitor and estimate building albedo and urban heat island impacts, and accurate representation of forests and vegetation can even be leveraged to better monitor the carbon cycle and climate change through improved land surface modelling. The advent of machine learning (ML) based CV techniques over the past decade provides a viable option to automate LULC mapping. One impediment to this has been the lack of large ML datasets. Large vector datasets for LULC are available, but can’t be used directly by ML practitioners due to a knowledge gap in transforming the input into a dataset of paired satellite images and segmentation masks. We demonstrate a novel end-to-end pipeline for LULC dataset creation that takes vector land cover data and provides a training-ready dataset. We will use Sentinel-2 satellite imagery and the European Urban Atlas LULC data. The pipeline manages everything from downloading satellite data, to creating and storing encoded segmentation masks and automating data checks. We then use the resulting dataset to train a semantic segmentation model. The aim of the pipeline is to provide a way for users to create their own custom datasets using various combinations of multispectral satellite and vector data. In addition to presenting the pipeline, we aim to provide an introduction to multispectral imagery, geospatial data and some of the challenges in using it for ML. Authors: Sambhav S Rohatgi (Spacesense.ai); Anthony Mucia (Spacesense.ai)	Computer Vision & Remote Sensing Cities & Urban Planning Climate Science & Modeling Forests Land Use
AAAI FSS 2022	AI-Based Text Analysis for Evaluating Food Waste Policies Abstract and authors: (click to expand) Abstract: Food waste is a major contributor to climate change, making the reduction of food waste one of the most important strategies to preserve threatened ecosystems and increase economic benefits. To evaluate the impact of food waste policies in this arena and provide actionable guidance to policymakers, we conducted an AI-based text analysis of food waste policy provisions. Specifically, we a) identified commonalities across state policy texts, b) clustered states by shared policy text, and c) examined relationships between state cluster memberships and food waste . This approach generated state clusters but demonstrated very limited convergent validity with policy ratings provided by subject matter experts and no predictive validity with food waste. We discuss the potential of using supervised machine learning to analyze food waste policy text as a next step. Authors: John Aitken (The MITRE Corporation), Denali Rao (The MITRE Corporation), Balca Alaybek (The MITRE Corporation), Amber Sprenger (The MITRE Corporation), Grace Mika (The MITRE Corporation), Rob Hartman (The MITRE Corporation) and Laura Leets (The MITRE Corporation)	Natural Language Processing Agriculture & Food
AAAI FSS 2022	Data-Driven Reduced-Order Model for Atmospheric CO2 Dispersion Abstract and authors: (click to expand) Abstract: Machine learning frameworks have emerged as powerful tools for the enhancement of computational fluid dynamics simulations and the construction of reduced-order models (ROMs). The latter are particularly desired when their full-order counterparts portray multiple spatiotemporal features and demand high processing power and storage capacity, such as climate models. In this work, a ROM for CO2 dispersion across Earth‘s atmosphere was built from NASA’s gridded daily OCO-2 carbon dioxide assimilated dataset. For that, a proper orthogonal decomposition was performed, followed by a non-intrusive operator inference (OpInf). This scientific machine learning technique was capable of accurately representing and predicting the detailed CO2 concentration field for about one year ahead, with a normalized root-mean-square error below 5%. It suggests OpInf-based ROMs may be a reliable alternative for fast response climate-related predictions. Authors: Pedro Roberto Barbosa Rocha (IBM Research), Marcos Sebastião de Paula Gomes (Pontifical Catholic University of Rio de Janeiro), João Lucas de Sousa Almeida (IBM Research), Allan Moreira Carvalho (IBM Research) and Alberto Costa Nogueira Junior (IBM Research)	Climate Science & Modeling
AAAI FSS 2022	KnowUREnvironment: An Automated Knowledge Graph for Climate Change and Environmental Issues Abstract and authors: (click to expand) Abstract: Despite climate change being one of the greatest threats to humanity, many people are still in denial or lack motivation for appropriate action. A structured source of knowledge can help increase public awareness while also helping crucial natural language understanding tasks such as information retrieval, question answering, and recommendation systems. We introduce KnowUREnvironment – a knowledge graph for climate change and related environmental issues, extracted from the scientific literature. We automatically identify 210,230 domain-specific entities/concepts and encode how these concepts are interrelated with 411,860 RDF triples backed up with evidence from the literature, without using any supervision or human intervention. Human evaluation shows our extracted triples are syntactically and factually correct (81.69% syntactic correctness and 75.85% precision). The proposed framework can be easily extended to any domain that can benefit from such a knowledge graph. Authors: Md Saiful Islam (University of Rochester), Adiba Proma (University of Rochester), Yilin Zhou (University of Rochester), Syeda Nahida Akter (Carnegie Mellon University), Caleb Wohn (University of Rochester) and Ehsan Hoque (University of Rochester)	Natural Language Processing
AAAI FSS 2022	Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms Abstract and authors: (click to expand) Abstract: Climate change is increasing the frequency and severity of harmful algal blooms (HABs), which cause significant fish deaths in aquaculture farms. This contributes to ocean pollution and greenhouse gas (GHG) emissions since dead fish are either dumped into the ocean or taken to landfills, which in turn negatively impacts the climate. Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope. This is a time-consuming, tedious and error-prone process, resulting in compromised management decisions by farmers. Hence, automating this process for quick and accurate HAB monitoring is extremely helpful. However, this requires large and diverse datasets of phytoplankton images, and such datasets are hard to produce quickly. In this work, we explore the feasibility of generating novel high-resolution photorealistic synthetic phytoplankton images, containing multiple species in the same image, given a small dataset of real images. To this end, we employ Generative Adversarial Networks (GANs) to generate synthetic images. We evaluate three different GAN architectures: ProjectedGAN, FastGAN, and StyleGANv2 using standard image quality metrics. We empirically show the generation of high-fidelity synthetic phytoplankton images using a training dataset of only 961 real images. Thus, this work demonstrates the ability of GANs to create large synthetic datasets of phytoplankton from small training datasets, accomplishing a key step towards sustainable systematic monitoring of harmful algal blooms. Authors: Nitpreet Bamra (University of Waterloo), Vikram Voleti (Mila, University of Montreal), Alexander Wong (University of Waterloo) and Jason Deglint (University of Waterloo)	Generative Modeling Computer Vision & Remote Sensing Oceans & Marine Systems Ecosystems & Biodiversity
AAAI FSS 2022	Generating physically-consistent high-resolution climate data with hard-constrained neural networks Abstract and authors: (click to expand) Abstract: The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often predict quantities at a coarse spatial resolution. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using methods from the super-resolution domain in computer vision. Despite often achieving visually compelling results, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: a renormalization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data. Authors: Paula Harder (Fraunhofer Institute ITWM, Mila Quebec AI Institute), Qidong Yang (Mila Quebec AI Institute, New York University), Venkatesh Ramesh (Mila Quebec AI Institute, University of Montreal), Alex Hernandez-Garcia (Mila Quebec AI Institute, University of Montreal), Prasanna Sattigeri (IBM Research), Campbell D. Watson (IBM Research), Daniela Szwarcman (IBM Research) and David Rolnick (Mila Quebec AI Institute, McGill University).	Climate Science & Modeling Weather & Climate Hybrid Physical Models
AAAI FSS 2022	Discovering Transition Pathways Towards Coviability with Machine Learning Abstract and authors: (click to expand) Abstract: This paper presents our ongoing French-Brazilian collaborative project which aims at: (1) establishing a diagnosis of socio-ecological coviability for several sites of interest in Nordeste, the North-East region of Brazil (in the states of Paraiba, Ceara, Pernambuco, and Rio Grande do Norte known for their biodiversity hotspots and vulnerabilities to climate change) using advanced data science techniques for multisource and multimodal data fusion and (2) finding transition pathways towards coviability equilibrium using machine learning techniques. Data collected in the field by scientists, ecologists, local actors combined with volunteered information, pictures from smart-phones, and data available on-line from satellite imagery, social media, surveys, etc. can be used to compute various coviability indicators of interest for the local actors. These indicators are useful to characterize and monitor the socio-ecological coviability status along various dimensions of anthropization, human welfare, ecological and biodiversity balance, and ecosystem intactness and vulnerabilities. Authors: Laure Berti-Equille (IRD) and Rafael Raimundo (UFPB)	Local and Indigenous Knowledge Systems Ecosystems & Biodiversity Reinforcement Learning Computer Vision & Remote Sensing
AAAI FSS 2022	Wildfire Forecasting with Satellite Images and Deep Generative Model Abstract and authors: (click to expand) Abstract: Wildfire prediction has been one of the most critical tasks that humanities want to thrive at. While it plays a vital role in protecting human life, it is also difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images into a video and used it to anticipate how the fire would behave in the future. However, creating video prediction models that account for the inherent uncertainty of the future is challenging. The bulk of published attempts are based on stochastic image-autoregressive recurrent networks, which raise various performance and application difficulties such as computational cost and limited efficiency on massive datasets. Another possibility is to use entirely latent temporal models that combine frame synthesis with temporal dynamics. However, due to design and training issues, no such model for stochastic video prediction has yet been proposed in the literature. This paper addresses these issues by introducing a novel stochastic temporal model whose dynamics are driven in a latent space. It naturally predicts video dynamics by allowing our lighter, more interpretable latent model to beat previous state-of-the-art approaches on the GOES-16 dataset. Results are compared using various benchmarking models. Authors: Thai-Nam Hoang (University of Wisconsin - Madison), Sang Truong (Stanford University) and Chris Schmidt (University of Wisconsin - Madison)	Computer Vision & Remote Sensing Generative Modeling Disaster Management and Relief
AAAI FSS 2022	From Ideas to Deployment - A Joint Industry-University Research Effort on Tackling Carbon Storage Challenges with AI Abstract and authors: (click to expand) Abstract: Carbon capture and storage (CCS) offers a promising means for significant reductions in greenhouse gas emissions and climate change mitigation at a large scale. Modeling CO2 transport and pressure buildup is central to understanding the responses of geosystems after CO2 injection and assessing the suitability and safety of CO2 storage. However, numerical simulations of geological CO2 storage often suffer from its multi-physics nature and complex non-linear governing equations, and is further complicated by flexible injection designs including changes in injection rates, resulting in formidable computational costs. New ideas have emerged such as data-driven models to tackle such challenges but very few have been fully developed and deployed as reliable tools. With the joint efforts of industry and universities, we are currently working on a new mechanism of fostering cross-disciplinary collaboration, developing, deploying, and scaling data-driven tools for CCS. A deep learning suite that can act as an alternative to CCS variable rate injection simulation will be the first tool developed under this mechanism. Based on the surrogate model, optimal design of injection strategy under pressure buildup constraints will be enabled with machine learning. Authors: Junjie Xu (Tsinghua University), Jiesi Lei (Tsinghua University), Yang Li (Tsinghua University), Junfan Ren (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing, China), Jian Qiu (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Biao Luo (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Lei Xiao (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China) and Wenwen Zhou (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China)	Carbon Capture & Sequestration
AAAI FSS 2022	NADBenchmarks - a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters Abstract and authors: (click to expand) Abstract: Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning(ML) for this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing the datasets according to the disaster management cycle. We compile a list of existing benchmark datasets that have been introduced in the past five years. We propose a web platform where researchers can search for benchmark datasets in this domain, and develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions in for topics where they can contribute new benchmark datasets. Authors: Adiba Proma (University of Rochester), Md Saiful Islam (University of Rochester), Stela Ciko (University of Rochester), Raiyan Abdul Baten (University of Rochester) and Ehsan Hoque (University of Rochester)	Disaster Management and Relief Data Mining
AAAI FSS 2022	Contrastive Learning for Climate Model Bias Correction and Super-Resolution Abstract and authors: (click to expand) Abstract: Climate models often require post-processing in order to make accurate estimates of local climate risk. The most common post-processing applied is bias-correction and spatial resolution enhancement. However, the statistical methods typically used for this not only are incapable of capturing multivariate spatial correlation information but are also reliant on rich observational data often not available outside of developed countries, limiting their potential. Here we propose an alternative approach to this challenge based on a combination of image super resolution (SR) and contrastive learning generative adversarial networks (GANs). We benchmark performance against NASA’s flagship post-processed CMIP6 climate model product, NEX-GDDP. We find that our model successfully reaches a spatial resolution double that of NASA’s product while also achieving comparable or improved levels of bias correction in both daily precipitation and temperature. The resulting higher fidelity simulations of present and forward-looking climate can enable more local, accurate models of hazards like flooding, drought, and heatwaves. Authors: Tristan Ballard (Sust Global) and Gopal Erinjippurath (Sust Global)	Climate Science & Modeling Generative Modeling
AAAI FSS 2022	Employing Deep Learning to Quantify Power Plant Greenhouse Gas Emissions via Remote Sensing Data Abstract and authors: (click to expand) Abstract: Greenhouse gasses (GHG) emitted from fossil-fuel-burning power plants pose a global threat to climate and public health. GHG emissions degrade air quality and increase the frequency of natural disasters five-fold, causing 8.7 million deaths per year. Quantifying GHG emissions is crucial for the success of the planet. However, current methods to track emissions cost upwards of $520,000/plant. These methods are cost prohibitive for developing countries, and are not globally standardized, leading to inaccurate emissions reports from nations and companies. I developed a low-cost solution via an end-to-end deep learning pipeline that utilizes observations of emitted smoke plumes in satellite imagery to provide an accurate, precise system for quantifying power plant GHG emissions by segmentation of power plant smoke plumes, classification of the plant fossil fuels, and algorithmic prediction of power generation and CO2 emissions. The pipeline was able to achieve a segmentation Intersection Over Union (IoU) score of 0.841, fuel classification accuracy of 92%, and quantify power generation and CO2 emission rates with R2 values of 0.852 and 0.824 respectively. The results of this work serve as a step toward the low-cost monitoring and detection of major sources of GHG emissions, helping limit their catastrophic impacts on climate and our planet. Authors: Aryan Jain (Amador Valley High School)	Computer Vision & Remote Sensing Earth Observation & Monitoring
AAAI FSS 2022	ClimateBert: A Pretrained Language Model for Climate-Related Text Abstract and authors: (click to expand) Abstract: Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP). However, while pretraining on general language has been shown to work very well for common language, it has been observed that niche language poses problems. In particular, climate-related texts include specific language that common LMs can not represent accurately. We argue that this shortcoming of today's LMs limits the applicability of modern NLP to the broad field of text processing of climate-related texts. As a remedy, we propose ClimateBert, a transformer-based language model that is further pretrained on over 2 million paragraphs of climate-related texts, crawled from various sources such as common news, research articles, and climate reporting of companies. We find that ClimateBert leads to a 48% improvement on a masked language model objective which, in turn, leads to lowering error rates by 3.57% to 35.71% for various climate-related downstream tasks like text classification, sentiment analysis, and fact-checking Authors: Nicolas Webersinke (FAU Erlangen-Nürnberg), Mathias Kraus (FAU Erlangen-Nürnberg), Julia Anna Bingler (ETH Zurich) and Markus Leippold (UZH Zurich)	Natural Language Processing
AAAI FSS 2022	Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning Abstract and authors: (click to expand) Abstract: Applying Machine learning to domains like Earth Sciences is impeded by the lack of labeled data, despite a large corpus of raw data available in such domains. For instance, training a wildfire classifier on satellite imagery requires curating a massive and diverse dataset, which is an expensive and time-consuming process that can span from weeks to months. Searching for relevant examples in over 40 petabytes of unlabelled data requires researchers to manually hunt for such images, much like finding a needle in a haystack. We present a no-code end-to-end pipeline, Curator, which dramatically minimizes the time taken to curate an exhaustive labeled dataset. Curator is able to search massive amounts of unlabelled data by combining self-supervision, scalable nearest neighbor search, and active learning to learn and differentiate image representations. The pipeline can also be readily applied to solve problems across different domains. Overall, the pipeline makes it practical for researchers to go from just one reference image to a comprehensive dataset in a diminutive span of time. Authors: Tarun Narayanan (SpaceML), Ajay Krishnan (SpaceML), Anirudh Koul (Pinterest, SpaceML, FDL) and Siddha Ganju (NVIDIA, SpaceML, FDL)	Data Mining Computer Vision & Remote Sensing
AAAI FSS 2022	De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images Abstract and authors: (click to expand) Abstract: With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO2 storage has shown promising results in its ability to monitor the growth of the CO2 plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO2 concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO2 plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO2 plumes by leveraging Class Activation Mapping (CAM) methods. Authors: Huseyin Tuna Erdinc (Georgia Institute of Technology), Abhinav Prakash Gahlot (Georgia Institute of Technology), Ziyi Yin (Georgia Institute of Technology), Mathias Louboutin (Georgia Institute of Technology) and Felix J. Herrmann (Georgia Institute of Technology)	Carbon Capture & Sequestration Interpretable ML Computer Vision & Remote Sensing
AAAI FSS 2022	Predicting Wildfire Risk Under Novel 21st-Century Climate Conditions Abstract and authors: (click to expand) Abstract: Wildfires are one of the most impactful hazards associated with climate change, and in a hotter, drier world, wildfires will be much more common than they have historically been. However, the exact severity and frequency of future wildfires are difficult to estimate, because climate change will create novel combinations of vegetation and fire weather outside what has been historically observed. This provides a challenge for AI-based approaches to long-term fire risk modeling, as much future fire risk is outside of the available feature space provided by the historical record. Here, we give an overview of this problem that is inherent to many climate change impacts and propose a restricted model form that makes monotonic and interpretable predictions in novel fire weather environments. We then show how our model outperforms other neural networks and logistic regression models when making predictions on unseen data from a decade into the future. Authors: Matthew Cooper (Sust Global).	Disaster Management and Relief Interpretable ML Uncertainty Quantification & Robustness
AAAI FSS 2022	Probabilistic Machine Learning in Polar Earth and Climate Science: A Review of Applications and Opportunities Abstract and authors: (click to expand) Abstract: Our world’s climate future is on thin ice. The study of longterm weather patterns in the polar regions is an important building block in tackling Climate Change. Our understanding of the past, the present and the future of the earth system, and the inherent uncertainty, informs planning, mitigation, and adaptation strategies. In this work we review previous applications of machine learning and statistical computing to polar climate research, and we highlight promising probabilistic machine learning methods that address the modelling needs of climate-related research in the Arctic and the Antarctic. We discuss common challenges in this interdisciplinary field and provide an overview of opportunities for future work in this novel area of research. Authors: Kim Bente (The University of Sydney), Judy Kay (The University of Sydney) and Roman Marchant (Commonwealth Scientific and Industrial Research Organisation (CSIRO))	Climate Science & Modeling Uncertainty Quantification & Robustness
AAAI FSS 2022	Rethinking Machine Learning for Climate Science: A Dataset Perspective Abstract and authors: (click to expand) Abstract: The growing availability of data sources is a predominant factor enabling the widespread success of machine learning (ML) systems across a wide range of applications. Typically, training data in such systems constitutes a source of ground-truth, such as measurements about a physical object (e.g., natural images) or a human artifact (e.g., natural language). In this position paper, we take a critical look at the validity of this assumption for datasets for climate science. We argue that many such climate datasets are uniquely biased due to the pervasive use of external simulation models (e.g., general circulation models) and proxy variables (e.g., satellite measurements) for imputing and extrapolating in-situ observational data. We discuss opportunities for mitigating the bias in the training and deployment of ML systems using such datasets. Finally, we share views on improving the reliability and accountability of ML systems for climate science applications. Authors: Aditya Grover (UCLA)	Data Mining
AAAI FSS 2022	Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints Abstract and authors: (click to expand) Abstract: To meet the mid-century goal of CO2 emissions reduction requires a rapid transformation of current electric power and natural gas (NG) infrastructure. This necessitates a long-term planning of the joint power-NG system under representative demand patterns, operational constraints, and policy considerations. Our work is motivated by the computational and practical challenges associated with solving the generation and transmission expansion problem (GTEP) for joint planning of power-NG systems. Specifically, we focus on efficiently extracting a set of representative days from power and NG demand data in respective networks and using this set to reduce the computational burden required to solve the GTEP. We propose a Graph Autoencoder for Multiple time resolution Energy Systems (GAMES) to capture the spatio-temporal demand patterns in interdependent networks and account for differences in the temporal resolution of available data. The resulting embeddings are used in a clustering algorithm to select representative days. We evaluate the effectiveness of our approach in solving a GTEP formulation calibrated for the joint power-NG system in New England. This formulation accounts for the physical interdependencies between power and NG systems, including the joint emissions constraint. Our results show that the set of representative days obtained from GAMES not only allows us to tractably solve the GTEP formulation, but also achieves a lower cost of implementing the joint planning decisions. Authors: Aron Brenner (MIT), Rahman Khorramfar (MIT), Dharik Mallapragada (MIT) and Saurabh Amin (MIT)	Power & Energy Time-series Analysis Optimization
AAAI FSS 2022	Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N Abstract and authors: (click to expand) Abstract: Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpins AI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org. Authors: Tianyu Zhang (Université de Montréal, MILA), Andrew Williams (Université de Montréal, MILA), Soham Phade (Salesforce Research), Sunil Srinivasa (Salesforce Research), Yang Zhang (MILA), Prateek Gupta (MILA, University of Oxford, The Alan Turing Institute), Yoshua Bengio (Université de Montréal, MILA, CIFAR) and Stephan Zheng (Salesforce Research)	Impact Assessment Public Policy
AAAI FSS 2022	Self-Supervised Representations of Geo-located Weather Time Series - an Evaluation and Analysis Abstract and authors: (click to expand) Abstract: Self-supervised learning (SSL) is gaining traction in various domains, and demonstrated their potential particularly in tasks where labelled data is limited and costly to collect. In this work, we evaluate the performance existing self-supervised multivariate time series learning algorithms on weather-driven applications involving regression, classification and forecasting tasks. We experiment with a two-step protocol. In the first step, we employ and SSL algorithm and learn generic weather representations from multivariate weather data. Then, in the next step, we use these representations and fine-tune them for multiple downstream tasks. Through our initial experiments on air quality prediction and renewable energy generation tasks, we highlight the benefits of self-supervised weather representations, including improved performance in such tasks, ability to generalize with limited in-task data, and reduction in training time and carbon emissions. We expect such a direction to be relevant in multiple weather-driven applications supporting climate change mitigation and adaptation efforts. Authors: Arjun Ashok (IBM Research), Devyani Lambhate (IBM Research) and Jitendra Singh (IBM Research)	Weather & Climate Time-series Analysis
AAAI FSS 2022	Predicting Daily Ozone Air Pollution With Transformers Abstract and authors: (click to expand) Abstract: Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Ozone at the surface also has considerable negative impacts on vegetation and crop yields. Ozone concentrations are affected by environmental factors, including temperature, which means that ozone concentrations are likely to change in future climates, posing risks to human health. This effect is known as the ozone climate penalty, and recent work suggests that currently polluted areas are likely to become more polluted by ozone in future climates. In light of recent stricter WHO regulations on surface ozone concentrations, we aim to build a predictive data-driven model for recent ozone concentrations, which could be used to make predictions of ozone concentrations in future climates, better quantifying future risks to human health and gaining insight into the variables driving ozone concentrations. We use observational station data from three European countries to train a transformer-based model to make predictions of daily maximum 8-hour ozone. Authors: Sebastian Hickman (University of Cambridge), Paul Griffiths (University of Cambridge), Peer Nowack (University of East Anglia) and Alex Archibald (University of Cambridge)	Weather & Climate
AAAI FSS 2022	The Impact of TCFD Reporting - A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures Abstract and authors: (click to expand) Abstract: We examine climate-related disclosures in 3,335 reports based on a sample of 188 banks that officially endorsed the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD). In doing so, we introduce a new application of zero-shot text classification based on the BART model and a MNLI task. By developing a set of robust and fine-grained labels, we show that zero-shot analysis provides high accuracy in analyzing companies’ climate-related reporting without further model training. We are able to demonstrate that banks that support the TCFD increase their level of disclosure after officially declaring their support for the guidelines, although we also find significant differences depending on the topic of disclosure. Our findings yield important conclusions for the design of climate-related disclosures. Authors: Alix Auzepy (Justus-Liebig-Universität Gießen), Elena Tönjes (Justus-Liebig-Universität Gießen) and Christoph Funk (Justus-Liebig-Universität Gießen)	Climate Finance & Economics Natural Language Processing
AAAI FSS 2022	Using Natural Language Processing for Automating the Identification of Climate Action Interlinkages within the Sustainable Development Goals Abstract and authors: (click to expand) Abstract: Climate action, Goal 13 of the UN Sustainable Development Goals (SDG), cuts across almost all SDGs. Achieving climate goals can reinforce the achievements in many other goals, but at the same time climate mitigation and adaptation measures may generate trade-offs, such as levelling the cost of energy and transitioning away from fossil fuels. Leveraging the synergies and minimizing the trade-offs among the climate goals and other SDGs is an imperative task for ensuring policy coherence. Understanding the interlinkages between climate action and other SDGs can help inform about the synergies and trade-offs. This paper presents a novel methodology by using natural language processing (NLP) to automate the process of systematically identifying the key interlinkages between climate action and SDGs from a large amount of climate literature. A qualitative SDG interlinkages model for climate action was automatically generated and visualized in a network graph. This work contributes to the conference thematic topic on using AI for policy alignment for climate change goals, SDGs and associated environmental, social and governance (ESG) frameworks. Authors: Xin Zhou (Institute for Global Environmental Strategies (IGES)), Kshitij Jain (Google Inc.), Mustafa Moinuddin (Institute for Global Environmental Strategies (IGES)) and Patrick McSharry (Carnegie Mellon University Africa; Oxford Man Institute of Quantitative Finance, Oxford University)	Natural Language Processing Climate Policy
AAAI FSS 2022	Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic Abstract and authors: (click to expand) Abstract: Streamflow observation data is vital for flood monitoring, agricultural, and settlement planning. However, such streamflow data are commonly plagued with missing observations due to various causes such as harsh environmental conditions and constrained operational resources. This problem is often more pervasive in under-resourced areas such as Sub-Saharan Africa. In this work, we reconstruct streamflow time series data through bias correction of the GEOGloWS ECMWF streamflow service (GESS) forecasts at ten river gauging stations in Benin. We perform bias correction by fitting Quantile Mapping, Gaussian Process, and Elastic Net regression in a constrained training period. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in low predictive skill over the ten Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior skill relative to traditional imputation by Random Forest, k-Nearest Neighbour, and GESS lookup. The findings of this work provide a basis for integrating global GESS streamflow data into operational early-warning decision-making systems (e.g., flood alert) in countries vulnerable to drought and flooding due to extreme weather events. Authors: Rendani Mbuvha (Queen Mary University of London), Julien Yise Peniel Adounkpe (International Water Management Institute (IWMI)), Wilson Tsakane Mongwe (University of Johannesburg), Mandela Houngnibo (Agence Nationale de la Météorologie du Benin Meteo Benin), Nathaniel Newlands (Summerland Research and Development Centre, Agriculture and Agri-Food Canada) and Tshilidzi Marwala (University of Johannesburg)	Extreme Weather Forecasting
AAAI FSS 2022	Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network Abstract and authors: (click to expand) Abstract: Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. Although recent geostationary satellite resolution has improved, the long-term analysis of climate applications is limited to using multiple satellites from the past to the present due to the different resolutions. To solve this problem, we proposed warp and refine network (WR-Net). WR-Net is divided into an optical flow warp component and a warp image refinement component.We used the TV-L1 algorithm instead of deep learning-based approaches to extract the optical flow warp component. The deep-learning-based model is trained on the human-centric view of the RGB channel and does not work on geostationary satellites, which is gray-scale one-channel imagery.The refinement network refines the warped image through a multi-temporal fusion layer. We evaluated WR-Net by interpolation of temporal resolution at 4 min intervals to 2 min intervals in large-scale GK2A geostationary meteorological satellite imagery. Furthermore, we applied WR-Net to the future frame prediction task and showed that the explicit use of optical flow can help future frame prediction. Authors: Minseok Seo (SI Analytics), Yeji Choi (SI Analytics), Hyungon Ryu (NVIDIA), Heesun Park (National Institute of Meteorological Science), Hyungkun Bae (SI Analytics), Hyesook Lee (National Institute of Meteorological Science) and Wanseok Seo (NVIDIA)	Computer Vision & Remote Sensing
AAAI FSS 2022	Machine Learning Methods in Climate Finance: A Systematic Review Abstract and authors: (click to expand) Abstract: Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the potential for the use of ML in climate finance and the proliferation of articles in this field, we propose a survey of the academic literature to assess how ML is enabling climate finance to scale up. The contribution of this paper is threefold. First, we do a systematic search in three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular application domains where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Secondly, we do an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area, aiming to spur further innovative work from ML experts. Authors: Andres Alonso-Robisco (Banco de España), Jose Manuel Carbo (Banco de España) and Jose Manuel Marques (Banco de España)	Climate Finance & Economics
NeurIPS 2021	Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Emerging Techniques in Computational Intelligence (ETCI) competition on Flood Detection tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, thereby progressively improving accuracy. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the provided high confidence hand-labeled data and, generated pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combine the generated labels with the previously available high confidence hand-labeled dataset. This assimilated dataset is used for the next round of training ensemble models. This cyclical process is repeated until the performance improvement plateaus. Additionally, we post process our results with Conditional Random Fields. Our approach sets a high score, and a new state-of-the-art on the Sentinel-1 dataset for the ETCI competition with 0.7654 IoU, an impressive improvement over the 0.60 IOU baseline. Our method, which we release with all the code including trained models, can also be used as an open science benchmark for the Sentinel-1 released dataset. Authors: Siddha Ganju (Nvidia Corporation); Sayak Paul (Carted)	Unsupervised & Semi-Supervised Learning Disaster Management and Relief Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2021	Short-term Solar Irradiance Prediction from Sky Images (Papers Track) Abstract and authors: (click to expand) Abstract: Solar irradiance forecasting is essential for the integration of the solar power into the power grid system while maintaining its stability. This paper focuses on short-term solar irradiance forecasting (upto 4-hour ahead-of-time prediction) from a past sky image sequence. Most existing work aims for the prediction of the most likely future of the solar irradiance. While it is likely deterministic for intra-hourly prediction, the future solar irradiance is naturally diverse over a relatively long-term horizon (>1h). We therefore introduce approaches to deterministic and stochastic predictions to capture the most likely as well as the diverse future of the solar irradiance. To enable the autoregressive prediction capability of the model, we proposed deep neural networks to predict the future sky images in a deterministic as well as stochastic manner. We evaluate our approaches on benchmark datasets and demonstrate that our approaches achieve superior performance. Authors: Hoang Chuong Nguyen (Australia National University); Miaomiao Liu (The Australian National University)	Computer Vision & Remote Sensing Power & Energy
NeurIPS 2021	Towards Representation Learning for Atmospheric Dynamics (Papers Track) Abstract and authors: (click to expand) Abstract: The prediction of future climate scenarios under anthropogenic forcing is critical to understand climate change and to assess the impact of potentially counter-acting technologies. Machine learning and hybrid techniques for this prediction rely on informative metrics that are sensitive to pertinent but often subtle influences. For atmospheric dynamics, a critical part of the climate system, no well established metric exists and visual inspection is currently still often used in practice. However, this ``eyeball metric'' cannot be used for machine learning where an algorithmic description is required. Motivated by the success of intermediate neural network activations as basis for learned metrics, e.g. in computer vision, we present a novel, self-supervised representation learning approach specifically designed for atmospheric dynamics. Our approach, called AtmoDist, trains a neural network on a simple, auxiliary task: predicting the temporal distance between elements of a randomly shuffled sequence of atmospheric fields (e.g. the components of the wind field from reanalysis or simulation). The task forces the network to learn important intrinsic aspects of the data as activations in its layers and from these hence a discriminative metric can be obtained. We demonstrate this by using AtmoDist to define a metric for GAN-based super resolution of vorticity and divergence. Our upscaled data matches both visually and in terms of its statistics a high resolution reference closely and it significantly outperform the state-of-the-art based on mean squared error. Since AtmoDist is unsupervised, only requires a temporal sequence of fields, and uses a simple auxiliary task, it has the potential to be of utility in a wide range of applications. Authors: Sebastian Hoffmann (University of Magdeburg); Christian Lessig (Otto-von-Guericke-Universitat Magdeburg)	Climate Science & Modeling Earth Observation & Monitoring Generative Modeling Time-series Analysis
NeurIPS 2021	Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion (Papers Track) Abstract and authors: (click to expand) Abstract: Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extreme events. Although machine learning approaches have shown promising results in long-range climate forecasting, the associated model uncertainties may reduce their reliability. To address this issue, we propose a late fusion approach that systematically combines the predictions from multiple models to reduce the expected errors of the fused results. We also propose a network architecture with the novel denormalization layer to gain the benefits of data normalization without actually normalizing the data. The experimental results on long-range 2m temperature forecasting show that the framework outperforms the 30-year climate normals, and the accuracy can be improved by increasing the number of models. Authors: Ken C. L. Wong (IBM Research – Almaden Research Center); Hongzhi Wang (IBM Almaden Research Center); Etienne E Vos (IBM); Bianca Zadrozny (IBM Research); Campbell D Watson (IBM Reserch); Tanveer Syeda-Mahmood (IBM Research)	Uncertainty Quantification & Robustness Climate Science & Modeling Time-series Analysis
NeurIPS 2021	Predicting Discharge in Catchment Outlet Using Deep Learning: Case Study of the Ansongo-Niamey Basin (Papers Track) Abstract and authors: (click to expand) Abstract: Hydrological models are one of the key challenges in hydrology. Their goal is to understand, predict and manage water resources. Most of the hydrological models so far were either physical or conceptual models. But in the past two decades, fully data-driven (empirical) models started to emerge with the breakthroughs of novel deep learning methods in runoff prediction. These breakthroughs were mostly favored by the large volume, variety and velocity of water-related data. Long Short-Term Memory and Gated Recurrent Unit neural networks, particularly achieved the outstanding milestone of outperforming classic hydrological models in less than a decade. Moreover, they have the potential to change the way hydrological modeling is performed. In this study, precipitation, minimal and maximum temperature at the Ansongo-Niamey basin combined with the discharge at Ansongo and Kandadji were used to predict the discharge at Niamey using artificial neural networks. After data preprocessing and hyperparameter optimization, the deep learning models performed well with the LSTM and GRU respectively scoring a Nash-Sutcliffe Efficiency of 0.933 and 0.935. This performance matches those of well-known physically-based models used to simulate Niamey’s discharge and therefore demonstrates the efficiency of deep learning methods in a West African context, especially in Niamey which has been facing severe floods due to climate change. Authors: Peniel J. Y. Adounkpe (WASCAL); Eric Alamou (Université d'Abomey-Calavi); Belko Diallo (WASCAL); Abdou Ali (AGRHYMET Regional Centre)	Disaster Management and Relief Causal & Bayesian Methods Time-series Analysis
NeurIPS 2021	Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods (Papers Track) Abstract and authors: (click to expand) Abstract: Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making. Authors: Dan Lu (Oak Ridge National Laboratory); Scott Painter (Oak Ridge National Laboratory); Nicholas Azzolina (University of North Dakota); Matthew Burton-Kelly (University of North Dakota)	Carbon Capture & Sequestration Uncertainty Quantification & Robustness
NeurIPS 2021	Towards debiasing climate simulations using unsuperviserd image-to-image translation networks (Papers Track) Abstract and authors: (click to expand) Abstract: Climate models form the basis of a vast portion of earth system research, and inform our climate policy. Due to the complex nature of our climate system, and the approximations which must necessarily be made in simulating it, these climate models may not perfectly match observations. For further research, these outputs must be bias corrected against observations, but current methods of debiasing do not take into account spatial correlations. We evaluate unsupervised image-to-image translation networks, specifically the UNIT model architecture, for their ability to produce more spatially realistic debiasing than the standard techniques used in the climate community. Authors: James Fulton (University of Edinburgh); Ben Clarke (Oxford University)	Climate Science & Modeling Generative Modeling
NeurIPS 2021	Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific (Papers Track) Abstract and authors: (click to expand) Abstract: Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN). An increase in CCN results in a decrease in the mean cloud droplet size (r$_{e}$). The smaller droplet size leads to brighter, more expansive, and longer lasting clouds that reflect more incoming sunlight, thus cooling the earth. Globally, aerosol-cloud interactions cool the Earth, however the strength of the effect is heterogeneous over different meteorological regimes. Understanding how aerosol-cloud interactions evolve as a function of the local environment can help us better understand sources of error in our Earth system models, which currently fail to reproduce the observed relationships. In this work we use recent non-linear, causal machine learning methods to study the heterogeneous effects of aerosols on cloud droplet radius. Authors: Andrew Jesson (University of Oxford); Peter Manshausen (University of Oxford); Alyson Douglas (University of Oxford); Duncan Watson-Parris (University of Oxford); Yarin Gal (University of Oxford); Philip Stier (University of Oxford)	Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2021	Memory to Map: Improving Radar Flood Maps With Temporal Context and Semantic Segmentation (Papers Track) Abstract and authors: (click to expand) Abstract: Global flood risk has increased due to worsening extreme weather events and human migration into growing flood-prone areas. Accurate, high-resolution, and near-real time flood maps can address flood risk by reducing financial loss and damage. We propose Model to Map, a novel machine learning approach that utilizes bi-temporal context to improve flood water segmentation performance for Sentinel-1 imagery. We show that the inclusion of unflooded context for the area, or "memory," allows the model to tap into a "prior state" of pre-flood conditions, increasing performance in geographic regions in which single-image radar-based flood mapping methods typically underperform (e.g. deserts). We focus on accuracy across different biomes to ensure global performance. Our experiments and novel data processing technique show that the confluence of pre-flood and permanent water context provides a 21% increase in mIoU over the baseline overall, and over 87% increase in deserts. Authors: Veda Sunkara (Cloud to Street); Nicholas Leach (Cloud to Street); Siddha Ganju (Nvidia)	Computer Vision & Remote Sensing Climate Justice Earth Observation & Monitoring
NeurIPS 2021	Hurricane Forecasting: A Novel Multimodal Machine Learning Framework (Papers Track) Abstract and authors: (click to expand) Abstract: This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-learning feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, and visual data such as reanalysis atmospheric images. We evaluate our models with current operational forecasts in North Atlantic (NA) and Eastern Pacific (EP) basins on 2016-2019 for 24-hour lead time, and show our models consistently outperform statistical-dynamical models and compete with the best dynamical models. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches. Authors: Léonard Boussioux (MIT, CentraleSupélec); Cynthia Zeng (MIT); Dimitris Bertsimas (MIT); Théo J Guenais (Harvard University)	Hybrid Physical Models Climate Science & Modeling Disaster Management and Relief Computer Vision & Remote Sensing Time-series Analysis Unsupervised & Semi-Supervised Learning
NeurIPS 2021	Improved Drought Forecasting Using Surrogate Quantile And Shape (SQUASH) Loss (Papers Track) Abstract and authors: (click to expand) Abstract: Droughts are amongst the most damaging natural hazard with cascading impacts across multiple sectors of the economy and society. Improved forecasting of drought conditions ahead of time can significantly improve strategic planning to mitigate the impacts and enhance resilience. Though significant progress in forecasting approaches has been made, the current approaches focus on the overall improvement of the forecast, with less attention on the extremeness of drought events. In this paper, we focus on improving the accuracy of forecasting extreme and severe drought events by introducing a novel loss function Surrogate Quantile and Shape loss (SQUASH) that combines weighted quantile loss and dynamic time-warping-based shape loss. We show the effectiveness of the proposed loss functions for imbalanced time-series drought forecasting tasks on two regions in India and the USA. Authors: Devyani Lambhate Lambhate (Indian Institute of Science); Smit Marvaniya (IBM Research India); Jitendra Singh (IBM Research - India); David Gold (IBM)	Disaster Management and Relief Climate Science & Modeling Time-series Analysis
NeurIPS 2021	Global ocean wind speed estimation with CyGNSSnet (Papers Track) Abstract and authors: (click to expand) Abstract: The CyGNSS (Cyclone Global Navigation Satellite System) satellite system measures GNSS signals reflected off the Earth's surface. A global ocean wind speed dataset is derived, which fills a gap in Earth observation data, will improve cyclone forecasting, and could be used to mitigate effects of climate change. We propose CyGNSSnet, a deep learning model for predicting wind speed from CyGNSS observables, and evaluate its potential for operational use. With CyGNSSnet, performance improves by 29\% over the current operational model. We further introduce a hierarchical model, that combines an extreme value classifier and a specialized CyGNSSnet and slightly improves predictions for high winds. Authors: Caroline Arnold (German Climate Computing Center); Milad Asgarimehr (German Research Centre for Geosciences)	Computer Vision & Remote Sensing Disaster Management and Relief Earth Observation & Monitoring
NeurIPS 2021	Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring (Papers Track) Abstract and authors: (click to expand) Abstract: The Biogeochemical-Argo (BGC-Argo) program is building a network of globally distributed, sensor-equipped robotic profiling floats, improving our understanding of the climate system and how it is changing. These floats, however, are limited in the number of variables measured. In this study, we train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location and apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network. We trained our neural networks on observations from the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP) and use dropout regularization to provide uncertainty bounds around our predicted values. Our neural network significantly improves upon linear regression but shows variable levels of uncertainty across the ranges of predicted variables. We explore the generalization of our estimators to test data outside our training distribution from both ESM and BGC-Argo data. Our use of out-of-distribution test data to examine shifts in biogeochemical parameters and calculate uncertainty bounds around estimates advance the state-of-the-art in oceanographic data and climate monitoring. We make our data and code publicly available. Authors: Ellen Park (MIT); Jae Deok Kim (MIT-WHOI); Nadege Aoki (MIT); Yumeng Cao (MIT); Yamin Arefeen (Massachusetts Institute of Technology); Matthew Beveridge (Massachusetts Institute of Technology); David P Nicholson (Woods Hole Oceanographic Institution); Iddo Drori (MIT)	Earth Observation & Monitoring Ecosystems & Biodiversity
NeurIPS 2021	On the Generalization of Agricultural Drought Classification from Climate Data (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon soil moisture index (SMI) obtained from a hydrological model. This index is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with land use information from MODIS satellite observation, we compare different models with and without sequential inductive bias in classifying droughts based on SMI. We use PR-AUC as the evaluation measure to account for the class imbalance and obtain promising results despite a challenging time-based split. We further show in an ablation study that the models retain their predictive capabilities given input data of coarser resolutions, as frequently encountered in climate models. Authors: Julia Gottfriedsen (1Deutsches Zentrum für Luft- und Raumfahrt (DLR), LMU); Max Berrendorf (Ludwig-Maximilians-Universität München); Pierre Gentine (Columbia University); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena); Katja Weigel (niversity of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany); Birgit Hassler (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Veronika Eyring (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany)	Climate Science & Modeling Disaster Management and Relief Agriculture & Food
NeurIPS 2021	Mapping Post-Climate Change Biogeographical Regions with Deep Latent Variable Models (Papers Track) Abstract and authors: (click to expand) Abstract: Forecasting future changes to biodiversity due to shifts in climate is challenging due to nonlinear interactions between species as recorded in their presence/absence data. This work proposes using variational autoencoders with environmental covariates to identify low-dimensional structure in species’ joint co-occurrence patterns and leveraging this simplified representation to provide multivariate predictions of their habitat extent under future climate scenarios. We pursue a latent space clustering approach to map biogeographical regions of frequently co-occurring species and apply this methodology to a dataset from northern Belgium, generating predictive maps illustrating how these regions may expand or contract with changing temperature under a future climate scenario. Authors: Christopher Krapu (Oak Ridge National Lab - Oak Ridge, TN)	Generative Modeling Causal & Bayesian Methods Land Use
NeurIPS 2021	Rotation Equivariant Deforestation Segmentation and Driver Classification (Papers Track) Abstract and authors: (click to expand) Abstract: Deforestation has become a significant contributing factor to climate change and, due to this, both classifying the drivers and predicting segmentation maps of deforestation has attracted significant interest. In this work, we develop a rotation equivariant convolutional neural network model to predict the drivers and generate segmentation maps of deforestation events from Landsat 8 satellite images. This outperforms previous methods in classifying the drivers and predicting the segmentation map of deforestation, offering a 9% improvement in classification accuracy and a 7% improvement in segmentation map accuracy. In addition, this method predicts stable segmentation maps under rotation of the input image, which ensures that predicted regions of deforestation are not dependent upon the rotational orientation of the satellite. Authors: Joshua Mitton (University of Glasgow); Roderick Murray-Smith (University of Glasgow)	Computer Vision & Remote Sensing
NeurIPS 2021	WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data (Papers Track) Abstract and authors: (click to expand) Abstract: The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from numerical weather prediction models, however, only have a spatial resolution of 10 to 20-km, which leads to sub-optimal usage and development of renewable energy farms. Weather scientists have been developing super-resolution methods to increase the resolution, but often rely on simple interpolation techniques or computationally expensive differential equation-based models. Recently, machine learning-based models, specifically the physics-informed resolution-enhancing generative adversarial network (PhIREGAN), have outperformed traditional downscaling methods. We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data. We accompany the benchmark with a novel public, processed, and machine learning-ready dataset for benchmarking super-resolution methods on wind and solar data. Authors: Rupa Kurinchi-Vendhan (Caltech); Björn Lütjens (MIT); Ritwik Gupta (University of California, Berkeley); Lucien D Werner (California Institute of Technology); Dava Newman (MIT); Steven Low (California Institute of Technology)	Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring Power & Energy Generative Modeling
NeurIPS 2021	Meta-Learned Bayesian Optimization for Calibrating Building Simulation Models with Multi-Source Data (Papers Track) Abstract and authors: (click to expand) Abstract: Well-calibrated building simulation models are key to reducing greenhouse gas emissions and optimizing building performance. Current calibration algorithms do not leverage data collected during previous calibration tasks. In this paper, we employ attentive neural processes (ANP) to meta-learn a distribution using multi-source data acquired during previously seen calibration tasks. The ANP informs a meta-learned Bayesian optimizer to accelerate calibration of new, unseen tasks. The few-shot nature of our proposed algorithm is demonstrated on a library of residential buildings validated by the United States Department of Energy (USDoE). Authors: Sicheng Zhan (NUS); Gordon Wichern (Mitsubishi Electric Research Laboratories (MERL)); Christopher Laughman (Mitsubishi Electric Research Laboratories); Ankush Chakrabarty (Mitsubishi Electric Research Labs)	Buildings Meta- and Transfer Learning
NeurIPS 2021	MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather (Papers Track) Abstract and authors: (click to expand) Abstract: We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours. By stacking a large input receptive field along the feature dimension and conditioning the model's forecaster with predictions from the physics-based High Resolution Rapid Refresh (HRRR) model, we are able to outperform optical flow and HRRR baselines by 20-25% on multiple metrics averaged over all lead times. Authors: Sylwester Klocek (Microsoft Corporation); Haiyu Dong (Microsoft); Matthew Dixon (Microsoft Corporation); Panashe Kanengoni (Microsoft Corporation); Najeeb Kazmi (Microsoft); Pete Luferenko (Microsoft Corporation); Zhongjian Lv (Microsoft Corporation); Shikhar Sharma (); Jonathan Weyn (Microsoft); Siqi Xiang (Microsoft Corporation)	Computer Vision & Remote Sensing Climate Science & Modeling Disaster Management and Relief Earth Observation & Monitoring Hybrid Physical Models
NeurIPS 2021	SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data (Papers Track) Abstract and authors: (click to expand) Abstract: When cloud layers cover photovoltaic (PV) panels, the amount of power the panels produce fluctuates rapidly. Therefore, to maintain enough energy on a power grid to match demand, utilities companies rely on reserve power sources that typically come from fossil fuels and therefore pollute the environment. Accurate short-term PV power prediction enables operators to maximize the amount of power obtained from PV panels and safely reduce the reserve energy needed from fossil fuel sources. While several studies have developed machine learning models to predict solar irradiance at specific PV generation facilities, little work has been done to model short-term solar irradiance on a global scale. Furthermore, models that have been developed are proprietary and have architectures that are not publicly available or rely on computationally demanding Numerical Weather Prediction (NWP) models. Here, we propose a Convolutional Long Short-Term Memory Network model that treats solar nowcasting as a next frame prediction problem, is more efficient than NWP models and has a straightforward, reproducible architecture. Our models can predict solar irradiance for entire North America for up to 3 hours in under 60 seconds on a single machine without a GPU and has a RMSE of 120 W/m^2 when evaluated on 2 months of data. Authors: Dhileeban Kumaresan (UC Berkeley); Richard Wang (UC Berkeley); Ernesto A Martinez (UC Berkeley); Richard Cziva (UC Berkeley); Alberto Todeschini (UC Berkeley); Colorado J Reed (University of California, Berkeley); Puya Vahabi (UC Berkeley)	Power & Energy Computer Vision & Remote Sensing
NeurIPS 2021	Synthetic Imagery Aided Geographic Domain Adaptation for Rare Energy Infrastructure Detection in Remotely Sensed Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Object detection in remotely sensed data is frequently stymied by applications in geographies that are different from that of the training data. When objects are rare, the problem is exacerbated further. This is true of assessments of energy infrastructure such as generation, transmission, and end-use consumption; key to electrification planning as well as for effective assessment of natural disaster impacts which are varying in frequency and intensity due to climate change. We propose an approach to domain adaptation that requires only unlabeled samples from the target domain and generates synthetic data to augment training data for targeted domain adaptation. This approach is shown to work consistently across four geographically diverse domains, improving object detection average precision by 15.5\% on average for small sample sizes. Authors: Wei Hu (Duke University); Tyler Feldman (Duke University); Eddy Lin (Duke University); Jose Luis Moscoso (Duke); Yanchen J Ou (Duke University); Natalie Tarn (Duke University); Baoyan Ye (Duke University); Wendy Zhang (Duke University); Jordan Malof (Duke University); Kyle Bradbury (Duke University)	Computer Vision & Remote Sensing Earth Observation & Monitoring Power & Energy
NeurIPS 2021	Being the Fire: A CNN-Based Reinforcement Learning Method to Learn How Fires Behave Beyond the Limits of Physics-Based Empirical Models (Papers Track) Abstract and authors: (click to expand) Abstract: Wildland fires pose an increasing threat in light of anthropogenic climate change. Fire-spread models play an underpinning role in many areas of research across this domain, from emergency evacuation to insurance analysis. We study paths towards advancing such models through deep reinforcement learning. Aggregating 21 fire perimeters from the Western United States in 2017, we construct 11-layer raster images representing the state of the fire area. A convolution neural network based agent is trained offline on one million sub-images to create a generalizable baseline for predicting the best action - burn or not burn - given the then-current state on a particular fire edge. A series of online, TD(0) Monte Carlo Q-Learning based improvements are made with final evaluation conducted on a subset of holdout fire perimeters. We examine the performance of the learned agent/model against the FARSITE fire-spread model. We also make available a novel data set and propose more informative evaluation metrics for future progress. Authors: William L Ross (Stanford)	Disaster Management and Relief Societal Adaptation & Resilience Computer Vision & Remote Sensing Reinforcement Learning
NeurIPS 2021	Subseasonal Solar Power Forecasting via Deep Sequence Learning (Papers Track) Abstract and authors: (click to expand) Abstract: To help mitigate climate change, power systems need to integrate renewable energy sources, such as solar, at a rapid pace. Widespread integration of solar energy into the power system requires major improvements in solar irradiance forecasting, in order to reduce the uncertainty associated with solar power output. While recent works have addressed short lead-time forecasting (minutes to hours ahead), week(s)-ahead and longer forecasts, coupled with uncertainty estimates, will be extremely important for storage applications in future power systems. In this work, we propose machine learning approaches for these longer lead-times as an important new application area in the energy domain. We demonstrate the potential of several deep sequence learning techniques for both point predictions and probabilistic predictions at these longer lead-times. We compare their performance for subseasonal forecasting (forecast lead-times of roughly two weeks) using the SURFRAD data set for 7 stations across the U.S. in 2018. The results are encouraging; the deep sequence learning methods outperform the current benchmark for machine learning-based probabilistic predictions (previously applied at short lead-times in this domain), along with relevant baselines. Authors: Saumya Sinha (University of Colorado, Boulder); Bri-Mathias S Hodge (University of Colorado Boulder); Claire Monteleoni (University of Colorado Boulder)	Power & Energy Time-series Analysis
NeurIPS 2021	A Transfer Learning-Based Surrogate Model for Geological Carbon Storage with Multi-Fidelity Training Data (Papers Track) Abstract and authors: (click to expand) Abstract: Geologic carbon storage (GCS) entails injecting large volumes of carbon dioxide (CO2) in deep geologic formations to prevent its release to the atmosphere. Reservoir simulation is widely used in GCS applications to predict subsurface pressure and CO2 saturation. High fidelity numerical models are prohibitively expensive for data assimilation and uncertainty quantification, which require a large number of simulation runs. Deep learning-based surrogate models have shown a great promise to alleviate the high computational cost. However, the training cost is high as thousands of high-fidelity simulations are often necessary for generating the training data. In this work, we explore the use of a transfer learning approach to reduce the training cost. Compared with the surrogate model trained with high-fidelity simulations, our new transfer learning-based model shows comparable accuracy but reduces the training cost by 80%. Authors: Su Jiang (Stanford University)	Carbon Capture & Sequestration Computer Vision & Remote Sensing
NeurIPS 2021	National Cropland Classification with Agriculture Census Information and EO Datasets (Papers Track) Abstract and authors: (click to expand) Abstract: National cropland classification is critical to monitor food security, comprehend environmental circumstances and climate change, and participate in agricultural policy development. The increasing earth observation datasets, especially the free available Sentinel and Landsat, open unprecedented large-scale mapping opportunities. However, most applied machine learning techniques have relied on substantial training datasets, which are not always available and may be expensive to create or collect. Focusing on Japan, this work indicates what kinds of information can be extracted from agriculture census information then used for mapping different crop types. Different classification approaches of pixel-based and parcel-based are compared. Then, the efficient method is used to generate Japan's first national cropland classification with Sentinel-1 C-band and Landsat-8 time series. For 2015, the overall accuracies for the prefectures range between 71\% and 94\%. This national cropland classification map, which particularly succeeds in extracting high-precision rice products for the whole of Japan and other classes for different prefectures, can be treated as the base map of Japan for future studies related to agriculture, environment, and climate change. Authors: Junshi Xia (RIKEN); Naoto Yokoya (The University of Tokyo); Bruno Adriano (RIKEN Center for Advanced Intelligence Project (AIP))	Agriculture & Food Computer Vision & Remote Sensing
NeurIPS 2021	FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection (Papers Track) Abstract and authors: (click to expand) Abstract: The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high fire-risk days, a small fire ignition can rapidly grow and get out of control. Early detection of fire ignitions from initial smoke can assist response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly-available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatio-temporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems to reduce the time to wildfire response. Authors: Anshuman Dewangan (University of California, San Diego); Mai Nguyen (University of California, San Diego); Garrison Cottrell (UC San Diego)	Disaster Management and Relief Computer Vision & Remote Sensing
NeurIPS 2021	Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: The burning of fossil fuels produces large amounts of carbon dioxide (CO2), a major Greenhouse Gas (GHG) and a main driver of Climate Change. Quantifying GHG emissions is crucial for accurate predictions of climate effects and to enforce emission trading schemes. The reporting of such emissions is only required in some countries, resulting in insufficient global coverage. In this work, we propose an end-to-end method to predict power generation rates for fossil fuel power plants from satellite images based on which we estimate GHG emission rates. We present a multitask deep learning approach able to simultaneously predict: (i) the pixel-area covered by plumes from a single satellite image of a power plant, (ii) the type of fired fuel, and (iii) the power generation rate. We then convert the predicted power generation rate into estimates for the rate at which CO2 is being emitted. Experimental results show that our model approach allows us to estimate the power generation rate of a power plant to within 139 MW (MAE, for a mean sample power plant capacity of 1177 MW) from a single satellite image and CO2 emission rates to within 311 t/h. This multitask learning approach improves the power generation estimation MAE by 39 % compared to a single-task network trained on the same dataset. Authors: Joëlle Hanna (University of St. Gallen); Michael Mommert (University of St. Gallen); Linus M. Scheibenreif (University of St. Gallen); Damian Borth (University of St. Gallen)	Computer Vision & Remote Sensing Power & Energy
NeurIPS 2021	ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models (Papers Track) Abstract and authors: (click to expand) Abstract: Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To fill this gap, we introduce the \climart dataset, with more than \emph{10 million samples from present, pre-industrial, and future climate conditions}. ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed. We also present several novel baselines that indicate shortcomings of the datasets and network architectures used in prior work. Authors: Salva Rühling Cachay (Technical University of Darmstadt); Venkatesh Ramesh (MILA); Jason N. S. Cole (Environment and Climate Change Canada); Howard Barker (Environment and Climate Change Canada); David Rolnick (McGill University, Mila)	Climate Science & Modeling
NeurIPS 2021	A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction (Papers Track) Best Paper: ML Innovation Abstract and authors: (click to expand) Abstract: Climate change poses new challenges to agricultural production, as crop yields are extremely sensitive to climatic variation. Accurately predicting the effects of weather patterns on crop yield is crucial for addressing issues such as food insecurity, supply stability, and economic planning. Recently, there have been many attempts to use machine learning models for crop yield prediction. However, these models either restrict their tasks to a relatively small region or a short time-period (e.g. a few years), which makes them hard to generalize spatially and temporally. They also view each location as an i.i.d sample, ignoring spatial correlations in the data. In this paper, we introduce a novel graph-based recurrent neural network for crop yield prediction, which incorporates both geographical and temporal structure. Our method is trained, validated, and tested on over 2000 counties from 41 states in the US mainland, covering years from 1981 to 2019. As far as we know, this is the first machine learning method that embeds geographical knowledge in crop yield prediction and predicts crop yields at the county level nationwide. Experimental results show that our proposed method consistently outperforms a wide variety of existing state-of-the-art methods, validating the effectiveness of geospatial and temporal information. Authors: Joshua Fan (Cornell University); Junwen Bai (Cornell University); Zhiyun Li (Cornell University); Ariel Ortiz-Bobea (Cornell); Carla P Gomes (Cornell University)	Agriculture & Food Time-series Analysis
NeurIPS 2021	Learned Benchmarks for Subseasonal Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: We develop a subseasonal forecasting toolkit of simple learned benchmark models that outperform both operational practice and state-of-the-art machine learning and deep learning methods. Our new models include (a) Climatology++, an adaptive alternative to climatology that, for precipitation, is 9% more accurate and 250\% more skillful than the United States operational Climate Forecasting System (CFSv2); (b) CFSv2++, a learned CFSv2 correction that improves temperature and precipitation accuracy by 7-8% and skill by 50-275%; and (c) Persistence++, an augmented persistence model that combines CFSv2 forecasts with lagged measurements to improve temperature and precipitation accuracy by 6-9% and skill by 40-130%. Across the contiguous U.S., these models consistently outperform standard meteorological baselines, state-of-the-art learning methods, and the European Centre for Medium-Range Weather Forecasts ensemble. Overall, we find that augmenting traditional forecasting approaches with learned enhancements yields an effective and computationally inexpensive strategy for building the next generation of subseasonal forecasting benchmarks. Authors: Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Miruna Oprescu (Microsoft Research); Judah Cohen (AER); Franklyn Wang (Harvard University); Sean Knight (MIT); Maria Geogdzhayeva (MIT); Sam Levang (Salient Predictions Inc.); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research)	Climate Science & Modeling
NeurIPS 2021	Emissions-aware electricity network expansion planning via implicit differentiation (Papers Track) Abstract and authors: (click to expand) Abstract: We consider a variant of the classical problem of designing or expanding an electricity network. Instead of minimizing only investment and production costs, however, we seek to minimize some mixture of cost and greenhouse gas emissions, even if the underlying dispatch model does not tax emissions. This enables grid planners to directly minimize consumption-based emissions, when expanding or modifying the grid, regardless of whether or not the carbon market incorporates a carbon tax. We solve this problem using gradient descent with implicit differentiation, a technique recently popularized in machine learning. To demonstrate the method, we optimize transmission and storage resources on the IEEE 14-bus test network and compare our solution to one generated by standard planning with a carbon tax. Our solution significantly reduces emissions for the same levelized cost of electricity. Authors: Anthony Degleris (Stanford University); Lucas Fuentes (Stanford); Abbas El Gamal (Stanford University); Ram Rajagopal (Stanford University)	Power & Energy Climate Policy
NeurIPS 2021	Amortized inference of Gaussian process hyperparameters for improved concrete strength trajectory prediction (Papers Track) Abstract and authors: (click to expand) Abstract: Designing and utilizing alternative concrete formulations which supplant the use of ordinary portland cement with alternative binders have been identified as central goals in reducing the greenhouse gas impact of the concrete industry. Given the variability in availability and quality of alternatives, these goals call for an optimal design of experiment approach to designing formulations, which can be adapted to local needs. The realization of this goal hinges on an ability to predict key properties. Here, we present and benchmark a Gaussian process (GP) model for predicting the trajectory of concrete strength, an essential performance measure. GPs are a desirable model class for the application because of their ability to estimate uncertainty and update predictions given additional data. In this work, rather than manually tuning hyperparameters for different concrete mix models, we propose a new method based on amortized inference leveraging mixture attributes, leading to models which are better fit for use in Bayesian optimization of concrete formulation. We demonstrate the success of the approach using a large, industrial concrete dataset. Authors: Kristen Severson (Microsoft Research); Olivia Pfeiffer (MIT); Jie Chen (IBM Research); Kai Gong (MIT); Jeremy Gregory (Massachusetts Institute of Technology); Richard Goodwin (IBM Research); Elsa Olivetti (Massachusetts Institute of Technology)	Causal & Bayesian Methods Heavy Industry and Manufacturing
NeurIPS 2021	A data integration pipeline towards reliable monitoring of phytoplankton and early detection of harmful algal blooms (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is making oceans warmer and more acidic. Under these conditions phytoplankton can produce harmful algal blooms which cause rapid oxygen depletion and consequent death of marine plants and animals. Some species are even capable of releasing toxic substances endangering water quality and human health. Monitoring of phytoplankton and early detection of harmful algal blooms is essential for protection of marine flaura and fauna. Recent technological advances have enabled in-situ plankton image capture in real-time at low cost. However, available phytoplankton image databases have several limitations that prevent the practical usage of artificial intelligent models. We present a pipeline for integration of heterogeneous phytoplankton image datasets from around the world into a unified database that can ultimately serve as a benchmark dataset for phytoplankton research and therefore act as an important tool in building versatile machine learning models for climate adaptation planning. A machine learning model for early detection of harmful algal blooms is part of ongoing work. Authors: Bruna Guterres (Universidade Federal do Rio Grande - FURG); Sara khalid (University of Oxford); Marcelo Pias (Federal University of Rio Grande); Silvia Botelho (Federal University of Rio Grande)	Disaster Management and Relief Ecosystems & Biodiversity
NeurIPS 2021	Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones (Papers Track) Abstract and authors: (click to expand) Abstract: Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite imagery) and model output (e.g., numerical weather prediction, statistical models) to produce forecasts every 6 hours. Within these time constraints, it can be challenging to draw insight from such data. While high-capacity machine learning methods are well suited for prediction problems with complex sequence data, extracting interpretable scientific information with such methods is difficult. Here we leverage powerful AI prediction algorithms and classical statistical inference to identify patterns in the evolution of TC convective structure leading up to the rapid intensification of a storm, hence providing forecasters and scientists with key insight into TC behavior. Authors: Irwin H McNeely (Carnegie Mellon University); Galen Vincent (Carnegie Mellon University); Rafael Izbicki (UFSCar); Kimberly Wood (Mississippi State University); Ann B. Lee (Carnegie Mellon University)	Disaster Management and Relief Interpretable ML
NeurIPS 2021	Predicting Atlantic Multidecadal Variability (Papers Track) Best Paper: Pathway to Impact Abstract and authors: (click to expand) Abstract: Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, therefore prediction of AMV, especially the extreme values, is of great societal utility for understanding and responding to regional climate change. This work tests multiple machine learning models to improve the state of AMV prediction from maps of sea surface temperature, salinity, and sea level pressure in the North Atlantic region. We use data from the Community Earth System Model 1 Large Ensemble Project, a state-of-the-art climate model with 3,440 years of data. Our results demonstrate that all of the models we use outperform the traditional persistence forecast baseline. Predicting the AMV is important for identifying future extreme temperatures and precipitation as well as hurricane activity, in Europe and North America up to 25 years in advance. Authors: Glenn Liu (Massachusetts Institute of Technology); Peidong Wang (MIT); Matthew Beveridge (Massachusetts Institute of Technology); Young-Oh Kwon (Woods Hole Oceanographic Institution); Iddo Drori (MIT)	Climate Science & Modeling
NeurIPS 2021	Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach (Papers Track) Abstract and authors: (click to expand) Abstract: Europe was hit by several, disastrous heat and drought events in recent summers. Besides thermodynamic influences, such hot and dry extremes are driven by certain atmospheric situations including anticyclonic conditions. Effects of climate change on atmospheric circulations are complex and many open research questions remain in this context, e.g., on future trends of anticyclonic conditions. Based on the combination of a catalog of labeled circulation patterns and spatial atmospheric variables, we propose a smoothed convolutional neural network classifier for six types of anticyclonic circulations that are associated with drought and heat. Our work can help to identify important drivers of hot and dry extremes in climate simulations, which allows to unveil the impact of climate change on these drivers. We address various challenges inherent to circulation pattern classification that are also present in other climate patterns, e.g., subjective labels and unambiguous transition periods. Authors: Magdalena Mittermeier (Ludwig-Maximilians-Universität München); Maximilian Weigert (Ludwig-Maximilians-Universität München); David Ruegamer (LMU Munich)	Climate Science & Modeling
NeurIPS 2021	Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images (Papers Track) Abstract and authors: (click to expand) Abstract: Wind energy is expected to be one of the leading ways to achieve the goals of the Paris Agreement but it in turn heavily depends on effective management of its operations and maintenance (O&M) costs. Blade failures account for one-third of all O&M costs thus making accurate detection of blade damages, especially cracks, very important for sustained operations and cost savings. Traditionally, damage inspection has been a completely manual process thus making it subjective, error-prone, and time-consuming. Hence in this work, we bring more objectivity, scalability, and repeatability in our damage inspection process, using deep learning, to miss fewer cracks. We build a deep learning model trained on a large dataset of blade damages, collected by our drone-based inspection, to correctly detect cracks. Our model is already in production and has processed more than a million damages with a recall of 0.96. We also focus on model interpretability using class activation maps to get a peek into the model workings. The model not only performs as good as human experts but also better in certain tricky cases. Thus, in this work, we aim to increase wind energy adoption by decreasing one of its major hurdles - the O&M costs resulting from missing blade failures like cracks. Authors: Akshay B Iyer (SkySpecs, Inc.); Linh V Nguyen (SkySpecs Inc); Shweta Khushu (SkySpecs Inc.)	Power & Energy Climate Finance & Economics Computer Vision & Remote Sensing
NeurIPS 2021	Evaluating Pretraining Methods for Deep Learning on Geophysical Imaging Datasets (Papers Track) Abstract and authors: (click to expand) Abstract: Machine learning has the potential to automate the analysis of vast amounts of raw geophysical data, allowing scientists to monitor changes in key aspects of our climate such as cloud cover in real-time and at fine spatiotemporal scales. However, the lack of large labeled training datasets poses a significant barrier for effectively applying machine learning to these applications. Transfer learning, which involves first pretraining a neural network on an auxiliary “source” dataset and then finetuning on the “target” dataset, has been shown to improve accuracy for machine learning models trained on small datasets. Across prior work on machine learning for geophysical imaging, different choices are made about what data to pretrain on, and the impact of these choices on model performance is unclear. To address this, we systematically explore various settings of transfer learning for cloud classification, cloud segmentation, and aurora classification. We pretrain on different source datasets, including the large ImageNet dataset as well as smaller geophysical datasets that are more similar to the target datasets. We also experiment with multiple transfer learning steps where we pretrain on more than one source dataset. Despite the smaller source datasets’ similarity to the target datasets, we find that pretraining on the large, general-purpose ImageNet dataset yields significantly better results across all of our experiments. Transfer learning is especially effective for smaller target datasets, and in these cases, using multiple source datasets can give a marginal added benefit. Authors: James Chen (Kirby School)	Computer Vision & Remote Sensing Earth Observation & Monitoring Meta- and Transfer Learning
NeurIPS 2021	An Automated System for Detecting Visual Damages of Wind Turbine Blades (Papers Track) Abstract and authors: (click to expand) Abstract: Wind energy’s ability to compete with fossil fuels on a market level depends on lowering wind’s high operational costs. Since damages on wind turbine blades are the leading cause for these operational problems, identifying blade damages is critical. However, recent works in visual identification of blade damages are still experimental and focus on optimizing the traditional machine learning metrics such as IoU. In this paper, we argue that pushing models to production long before achieving the "optimal" model performance can still generate real value for this use case. We discuss the performance of Blue, our damage’s suggestion model in production and how this system works in coordination with humans as part of a commercialized product and how it can contribute towards lowering wind energy’s operational costs. Authors: Linh V Nguyen (SkySpecs Inc); Akshay B Iyer (SkySpecs, Inc.); Shweta Khushu (SkySpecs Inc.)	Computer Vision & Remote Sensing Climate Finance & Economics
NeurIPS 2021	Predicting Power System Dynamics and Transients: A Frequency Domain Approach (Papers Track) Abstract and authors: (click to expand) Abstract: With the ambition of reducing carbon emissions and mitigating climate change, many regions have set up the goal to generate electricity with close to 100% renewables. However, actual renewable generations are often curtailed by operators because it is too hard to check the dynamic stability of the electric grid under the high uncertainties introduced by the renewables. The dynamics of a power grid are governed by a large number of nonlinear ordinary differential equations (ODEs). To safely operate the system, operators need to check that the states described by this set of ODEs stay within prescribed limits after various potential faults. Limited by the size and stiffness of the ODEs, current numerical integration techniques are often too slow to be useful in real-time or large-scale resource allocation problems. In addition, detailed system parameters are often not exactly known. Machine learning approaches have been proposed to reduce the computational efforts, but existing methods generally suffer from overfitting and failures to predict unstable behaviors. This paper proposes a novel framework for power system dynamic predictions by learning in the frequency domain. The intuition is that although the system behavior is complex in the time domain, there are relatively few dominate modes in the frequency domain. Therefore, we learn to predict by constructing neural networks with Fourier transform and filtering layers. System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial (across different buses) frequencies. We show that the proposed approach does not need detailed system parameters, speeds up prediction computations by orders of magnitude and is highly accurate for different fault types. Authors: Wenqi Cui (University of Washington); Weiwei Yang (Microsoft Research); Baosen Zhang (University of Washington)	Power & Energy
NeurIPS 2021	HyperionSolarNet: Solar Panel Detection from Aerial Images (Papers Track) Abstract and authors: (click to expand) Abstract: With the effects of global climate change impacting the world, collective efforts are needed to reduce greenhouse gas emissions. The energy sector is the single largest contributor to climate change and many efforts are focused on reducing dependence on carbon-emitting power plants and moving to renewable energy sources, such as solar power. A comprehensive database of the location of solar panels is important to assist analysts and policymakers in defining strategies for further expansion of solar energy. In this paper we focus on creating a world map of solar panels. We identify locations and total surface area of solar panels within a given geographic area. We use deep learning methods for automated detection of solar panel locations and their surface area using aerial imagery. The framework, which consists of a two-branch model using an image classifier in tandem with a semantic segmentation model, is trained on our created dataset of satellite images. Our work provides an efficient and scalable method for detecting solar panels, achieving an accuracy of 0.96 for classification and an IoU score of 0.82 for segmentation performance. Authors: Poonam Parhar (UCBerkeley); Ryan Sawasaki (UCBerkeley); Alberto Todeschini (UC Berkeley); Colorado Reed (UC Berkeley); Hossein Vahabi (University California Berkeley); Nathan Nusaputra (UC Berkeley); Felipe Vergara (UC Berkeley)	Power & Energy Buildings Climate Policy Computer Vision & Remote Sensing
NeurIPS 2021	Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model (Papers Track) Abstract and authors: (click to expand) Abstract: In combating climate change, an effective demand-based energy supply operation of the district energy system (DES) for heating or cooling is indispensable. As a consequence, an accurate forecast of heat consumption on the consumer side poses an important first step towards an optimal energy supply. However, due to the non-linearity and non-stationarity of heat consumption data, the prediction of the thermal energy demand of DES remains challenging. In this work, we propose a forecasting framework for thermal energy consumption within a district heating system (DHS) based on kernel Support Vector Regression (kSVR) using real-world smart meter data. Particle Swarm Optimization (PSO) is employed to find the optimal hyper-parameter for the kSVR model which leads to the superiority of the proposed methods when compared to a state-of-the-art ARIMA model. The average MAPE is reduced to 2.07% and 2.64% for the individual meter-specific forecasting and for forecasting of societal consumption, respectively. Authors: Satyaki Chatterjee (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Siming Bayer (Pattern Recognition Lab, Friedrich-Alexander University); Andreas K Maier (Pattern Recognition Lab, FAU Erlangen-Nuremberg)	Power & Energy Time-series Analysis
NeurIPS 2021	EcoLight: Reward Shaping in Deep Reinforcement Learning for Ergonomic Traffic Signal Control (Papers Track) Abstract and authors: (click to expand) Abstract: Mobility, the environment, and human health are all harmed by sub-optimal control policies in transportation systems. Intersection traffic signal controllers are a crucial part of today's transportation infrastructure, as sub-optimal policies may lead to traffic jams and as a result increased levels of air pollution and wasted time. Many adaptive traffic signal controllers have been proposed in the literature, but research on their relative performance differences is limited. On the other hand, to the best of our knowledge there has been no work that directly targets CO2 emission reduction, even though pollution is currently a critical issue. In this paper, we propose a reward shaping scheme for various RL algorithms that not only produces lowers CO2 emissions, but also produces respectable outcomes in terms of other metrics such as travel time. We compare multiple RL algorithms --- sarsa, and A2C --- as well as diverse scenarios with a mix of different road users emitting varied amounts of pollution. Authors: Pedram Agand (Simon Fraser University); Alexey Iskrov (Breeze Labs Inc.); Mo Chen (Simon Fraser University)	Transportation Power & Energy Reinforcement Learning
NeurIPS 2021	Data Driven Study of Estuary Hypoxia (Papers Track) Abstract and authors: (click to expand) Abstract: This paper presents a data driven study of dissolved oxygen times series collected in Atlantic Canada. The main motivation of presented work was to evaluate if machine learning techniques could help to understand and anticipate hypoxic episodes in nutrient-impacted estuaries, a phenomenon that is exacerbated by increasing temperature expected to arise due to changes in climate. A major constraint was to limit ourselves to the use of dissolved oxygen time series only. Our preliminary findings shows that recurring neural networks and in particular LSTM may be suitable to predict short horizon levels while traditional results could benefit in longer range hypoxia prevention. Authors: Md Monwer Hussain (University of New-Brunswick); Guillaume Durand (National Research Council Canada); Michael Coffin (Department of Fisheries and Oceans Canada); Julio J Valdés (National Research Council Canada); Luke Poirier (Department of Fisheries and Oceans Canada)	Ecosystems & Biodiversity Time-series Analysis
NeurIPS 2021	Decentralized Safe Reinforcement Learning for Voltage Control (Papers Track) Abstract and authors: (click to expand) Abstract: Inverter-based distributed energy resources provide the possibility for fast time-scale voltage control by quickly adjusting their reactive power. The power-electronic interfaces allow these resources to realize almost arbitrary control law, but designing these decentralized controllers is nontrivial. Reinforcement learning (RL) approaches are becoming increasingly popular to search for policy parameterized by neural networks. It is difficult, however, to enforce that the learned controllers are safe, in the sense that they may introduce instabilities into the system. This paper proposes a safe learning approach for voltage control. We prove that the system is guaranteed to be exponentially stable if each controller satisfies certain Lipschitz constraints. The set of Lipschitz bound is optimized to enlarge the search space for neural network controllers. We explicitly engineer the structure of neural network controllers such that they satisfy the Lipschitz constraints by design. A decentralized RL framework is constructed to train local neural network controller at each bus in a model-free setting. Authors: Wenqi Cui (University of Washington); Jiayi Li (University of Washington); Baosen Zhang (University of Washington)	Power & Energy Reinforcement Learning
NeurIPS 2021	NoFADE: Analyzing Diminishing Returns on CO2 Investment (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change continues to be a pressing issue that currently affects society at-large. It is important that we as a society, including the Computer Vision (CV) community take steps to limit our impact on the environment. In this paper, we (a) analyze the effect of diminishing returns on CV methods, and (b) propose a \textit{``NoFADE''}: a novel entropy-based metric to quantify model--dataset--complexity relationships. We show that some CV tasks are reaching saturation, while others are almost fully saturated. In this light, NoFADE allows the CV community to compare models and datasets on a similar basis, establishing an agnostic platform. Authors: Andre Fu (University of Toronto); Justin B Tran (University of Toronto); Andy Xie (University of Toronto); Jonathan T Spraggett (University of Toronto); Elisa Ding (University of Toronto); Chang-Won Lee (University of Toronto); Kanav Singla (University of Toronto); Mahdi S. Hosseini (University of New Brunswick); Konstantinos N Plataniotis (UofT)	Carbon Capture & Sequestration Computer Vision & Remote Sensing
NeurIPS 2021	High-resolution rainfall-runoff modeling using graph neural network (Papers Track) Abstract and authors: (click to expand) Abstract: Time-series modeling has shown great promise in recent studies using the latest deep learning algorithms such as LSTM (Long Short-Term Memory). These studies primarily focused on watershed-scale rainfall-runoff modeling or streamflow forecasting, but the majority of them only considered a single watershed as a unit. Although this simplification is very effective, it does not take into account spatial information, which could result in significant errors in large watersheds. Several studies investigated the use of GNN (Graph Neural Networks) for data integration by decomposing a large watershed into multiple sub-watersheds, but each sub-watershed is still treated as a whole, and the geoinformation contained within the watershed is not fully utilized. In this paper, we propose the GNRRM (Graph Neural Rainfall-Runoff Model), a novel deep learning model that makes full use of spatial information from high-resolution precipitation data, including flow direction and geographic information. When compared to baseline models, GNRRM has less over-fitting and significantly improves model performance. Our findings support the importance of hydrological data in deep learning-based rainfall-runoff modeling, and we encourage researchers to include more domain knowledge in their models. Authors: Zhongrun Xiang (University of Iowa); Ibrahim Demir (The University of Iowa)	Time-series Analysis Climate Science & Modeling Disaster Management and Relief Earth Observation & Monitoring Ecosystems & Biodiversity Data Mining
NeurIPS 2021	Machine Learning for Snow Stratigraphy Classification (Papers Track) Abstract and authors: (click to expand) Abstract: Snow-layer segmentation and classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. To this end a Snow Micro Pen (SMP) can be used - a portable high-resolution snow penetrometer. However, the penetration-force measurements of the SMP must be labeled manually, which is a time-intensive task that requires training and becomes infeasible for large datasets. Here, we evaluate how well machine learning models can automatically segment and classify SMP profiles. Fourteen different models are trained on the MOSAiC SMP dataset, a unique and large SMP dataset of snow on Arctic sea-ice profiles. Depending on the user's task and needs, the long short-term memory neural network and the random forests are performing the best. The findings presented here facilitate and accelerate SMP data analysis and in consequence, help scientists to analyze the effects of climate change on the cryosphere more efficiently. Authors: Julia Kaltenborn (McGill University); Viviane Clay (Osnabrück University); Amy R. Macfarlane (WSL Institute for Snow and Avalanche Research SLF); Martin Schneebeli (WSL Institute for Snow and Avalanche Research SLF)	Earth Observation & Monitoring Time-series Analysis
NeurIPS 2021	DEM Super-Resolution with EfficientNetV2 (Papers Track) Abstract and authors: (click to expand) Abstract: Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisition of high-quality data for many environmental disciplines is costly. Digital Elevation Model (DEM) datasets are such examples whereas their low-resolution versions are widely available, high-resolution ones are scarce. In an effort to rectify this problem, we propose and assess an EfficientNetV2 based model. The proposed model increases the spatial resolution of DEMs up to 16 times without additional information. Authors: Bekir Z Demiray (University of Iowa); Muhammed A Sit (The University of Iowa); Ibrahim Demir (The University of Iowa)	Computer Vision & Remote Sensing Disaster Management and Relief Earth Observation & Monitoring
NeurIPS 2021	Learning to Dissipate Traffic Jams with Piecewise Constant Control (Papers Track) Abstract and authors: (click to expand) Abstract: Greenhouse gases (GHGs), particularly carbon dioxide, are a key contributor to climate change. The transportation sector makes up 35% of CO2 emissions in the US and more than 70% of it is due to land transport. Previous work shows that simple driving interventions have the ability to significantly improve traffic flow on the road. Recent work shows that 5% of vehicles using piecewise constant controllers, designed to be compatible to the reaction times of human drivers, can prevent the formation of stop-and-go traffic congestion on a single-lane circular track, thereby mitigating land transportation emissions. Our work extends these results to consider more extreme traffic settings, where traffic jams have already formed, and environments with limited cooperation. We show that even with the added realism of these challenges, piecewise constant controllers, trained using deep reinforcement learning, can essentially eliminate stop-and-go traffic when actions are held fixed for up to 5 seconds. Even up to 10-second action holds, such controllers show congestion benefits over a human driving baseline. These findings are a stepping-stone for near-term deployment of vehicle-based congestion mitigation. Authors: Mayuri Sridhar (MIT); Cathy Wu ()	Transportation Reinforcement Learning
NeurIPS 2021	Multi-objective Reinforcement Learning Controller for Multi-Generator Industrial Wave Energy Converter (Papers Track) Abstract and authors: (click to expand) Abstract: Waves are one of the greatest sources of renewable energy and are a promising resource to tackle climate challenges by decarbonizing energy generation. Lowering the Levelized Cost of Energy (LCOE) for wave energy converters is key to competitiveness with other forms of clean energy like wind and solar. Also, the complexity of control has gone up significantly with the state-of-the-art multi-generator multi-legged industrial Wave Energy Converters (WEC). This paper introduces a Multi-Agent Reinforcement Learning controller (MARL) architecture that can handle these multiple objectives for LCOE, helping the increase in energy capture efficiency, boosting revenue, reducing structural stress to limit maintenance and operating cost, and adaptively and proactively protect the wave energy converter from catastrophic weather events, preserving investments and lowering effective capital cost. We use a MARL implementing proximal policy optimization (PPO) with various optimizations to help sustain the training convergence in the complex hyperplane. The MARL is able to better control the reactive forces of the generators on multiple tethers (legs) of WEC than the commonly deployed spring damper controller. The design for trust is implemented to assure the operation of WEC within a safe zone of mechanical compliance and guarantee mechanical integrity. This is achieved through reward shaping for multiple objectives of energy capture and penalty for harmful motions to minimize stress and lower the cost of maintenance. We achieved double-digit gains in energy capture efficiency across the waves of different principal frequencies over the baseline Spring Damper controller with the proposed MARL controllers. Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Alexander Shmakov (UC Irvine); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Paolo Faraboschi (HPE); mathieu Cocho (Carnegie Clean Energy); Alexandre Pichard (Carnegie Clean Energy); Jonathan Fievez (Carnegie Clean Energy)	Power & Energy Reinforcement Learning
NeurIPS 2021	Resolving Super Fine-Resolution SIF via Coarsely-Supervised U-Net Regression (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change presents challenges to crop productivity, such as increasing the likelihood of heat stress and drought. Solar-Induced Chlorophyll Fluorescence (SIF) is a powerful way to monitor how crop productivity and photosynthesis are affected by changing climatic conditions. However, satellite SIF observations are only available at a coarse spatial resolution (e.g. 3-5km) in most places, making it difficult to determine how individual crop types or farms are doing. This poses a challenging coarsely-supervised regression task; at training time, we only have access to SIF labels at a coarse resolution (3 km), yet we want to predict SIF at a very fine spatial resolution (30 meters), a 100x increase. We do have some fine-resolution input features (such as Landsat reflectance) that are correlated with SIF, but the nature of the correlation is unknown. To address this, we propose Coarsely-Supervised Regression U-Net (CSR-U-Net), a novel approach to train a U-Net for this coarse supervision setting. CSR-U-Net takes in a fine-resolution input image, and outputs a SIF prediction for each pixel; the average of the pixel predictions is trained to equal the true coarse-resolution SIF for the entire image. Even though this is a very weak form of supervision, CSR-U-Net can still learn to predict accurately, due to its inherent localization abilities, plus additional enhancements that facilitate the incorporation of scientific prior knowledge. CSR-U-Net can resolve fine-grained variations in SIF more accurately than existing averaging-based approaches, which ignore fine-resolution spatial variation during training. CSR-U-Net could also be useful for a wide range of "downscaling'" problems in climate science, such as increasing the resolution of global climate models. Authors: Joshua Fan (Cornell University); Di Chen (Cornell University); Jiaming Wen (Cornell University); Ying Sun (Cornell University); Carla P Gomes (Cornell University)	Ecosystems & Biodiversity Computer Vision & Remote Sensing Unsupervised & Semi-Supervised Learning Agriculture & Food
NeurIPS 2021	PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities (Papers Track) Abstract and authors: (click to expand) Abstract: The machine learning community has recently had increased interest in the climate and disaster damage domain due to a marked increased occurrences of natural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However, not enough attention has been devoted to mitigating probable destruction from impending natural hazards. We explore this crucial space by predicting building-level damages on a before-the-fact basis that would allow state actors and non-governmental organizations to be best equipped with resource distribution to minimize or preempt losses. We introduce PreDisM that employs an ensemble of ResNets and fully connected layers over decision trees to capture image-level and meta-level information to accurately estimate weakness of man-made structures to disaster-occurrences. Our model performs well and is responsive to tuning across types of disasters and highlights the space of preemptive hazard damage modelling. Authors: Vishal Anand (Columbia University); Yuki Miura (Columbia University)	Disaster Management and Relief Buildings Computer Vision & Remote Sensing Interpretable ML
NeurIPS 2021	A hybrid convolutional neural network/active contour approach to segmenting dead trees in aerial imagery (Papers Track) Abstract and authors: (click to expand) Abstract: The stability and ability of an ecosystem to withstand climate change is directly linked to its biodiversity. Dead trees are a key indicator of overall forest health, housing one-third of forest ecosystem biodiversity, and constitute 8% of the global carbon stocks. They are decomposed by several natural factors, e.g. climate, insects and fungi. Accurate detection and modeling of dead wood mass is paramount to understanding forest ecology, the carbon cycle and decomposers. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established convolutional neural networks with a novel active contour model in an energy minimization framework. Our approach yields superior performance accuracy over state-of-the-art in terms of precision, recall, and intersection over union of detected dead trees. This improved performance is essential to meet emerging challenges caused by climate change (and other man-made perturbations to the systems), particularly to monitor and estimate carbon stock decay rates, monitor forest health and biodiversity, and the overall effects of dead wood on and from climate change. Authors: Jacquelyn Shelton (Hong Kong Polytechnic University); Przemyslaw Polewski (TomTom Location Technology Germany GmbH); Wei Yao (The Hong Kong Polytechnic University); Marco Heurich (Bavarian Forest National Park)	Computer Vision & Remote Sensing Carbon Capture & Sequestration Ecosystems & Biodiversity Forests
NeurIPS 2021	Semi-Supervised Classification and Segmentation on High Resolution Aerial Images (Papers Track) Abstract and authors: (click to expand) Abstract: FloodNet is a high-resolution image dataset acquired by a small UAV platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a unique challenge of advancing the damage assessment process for post-disaster scenarios using unlabeled and limited labeled dataset. We propose a solution to address their classification and semantic segmentation challenge. We approach this problem by generating pseudo labels for both classification and segmentation during training and slowly incrementing the amount by which the pseudo label loss affects the final loss. Using this semi-supervised method of training helped us improve our baseline supervised loss by a huge margin for classification, allowing the model to generalize and perform better on the validation and test splits of the dataset. In this paper, we compare and contrast the various methods and models for image classification and semantic segmentation on the FloodNet dataset. Authors: Sahil S Khose (Manipal Institute of Technology); Abhiraj Tiwari (Manipal Institute of Technology); Ankita Ghosh (Manipal Institute of Technology)	Disaster Management and Relief Meta- and Transfer Learning Unsupervised & Semi-Supervised Learning
NeurIPS 2021	Reducing the Barriers of Acquiring Ground-truth from Biodiversity Rich Audio Datasets Using Intelligent Sampling Techniques (Papers Track) Abstract and authors: (click to expand) Abstract: The potential of passive acoustic monitoring (PAM) as a method to reveal the consequences of climate change on the biodiversity that make up natural soundscapes can be undermined by the discrepancy between the low barrier of entry to acquire large field audio datasets and the higher barrier of acquiring reliable species level training, validation, and test subsets from the field audio. These subsets from a deployment are often required to verify any machine learning models used to assist researchers in understanding the local biodiversity. Especially as many models convey promising results from various sources that may not translate to the collected field audio. Labeling such datasets is a resource intensive process due to the lack of experts capable of identifying bioacoustics at a species level as well as the overwhelming size of many PAM audiosets. To address this challenge, we have tested different sampling techniques on an audio dataset collected over a two-week long August audio array deployment on the Scripps Coastal Reserve (SCR) Biodiversity Trail in La Jolla, California. These sampling techniques involve creating four subsets using stratified random sampling, limiting samples to the daily bird vocalization peaks, and using a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) trained for bird presence/absence audio classification. We found that a stratified random sample baseline only achieved a bird presence rate of 44% in contrast with a sample that randomly selected clips with high hybrid CNN-RNN predictions that were collected during bird activity peaks at dawn and dusk yielding a bird presence rate of 95%. The significantly higher bird presence rate demonstrates how intelligent, machine learning-assisted selection of audio data can significantly reduce the amount of time that domain experts listen to audio without vocalizations of interest while building a ground truth for machine learning models. Authors: Jacob G Ayers (UC San Diego); Sean Perry (UC San Diego); Vaibhav Tiwari (UC San Diego); Mugen Blue (Cal Poly San Luis Obispo); Nishant Balaji (UC San Diego); Curt Schurgers (UC San Diego); Ryan Kastner (University of California San Diego); Mathias Tobler (San Diego Zoo Wildlife Alliance); Ian Ingram (San Diego Zoo Wildlife Alliance)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2021	Two-phase training mitigates class imbalance for camera trap image classification with CNNs (Papers Track) Abstract and authors: (click to expand) Abstract: By leveraging deep learning to automatically classify camera trap images, ecologists can monitor biodiversity conservation efforts and the effects of climate change on ecosystems more efficiently. Due to the imbalanced class-distribution of camera trap datasets, current models are biased towards the majority classes. As a result, they obtain good performance for a few majority classes but poor performance for many minority classes. We used two-phase training to increase the performance for these minority classes. We trained, next to a baseline model, four models that implemented a different versions of two-phase training on a subset of the highly imbalanced Snapshot Serengeti dataset. Our results suggest that two-phase training can improve performance for many minority classes, with limited loss in performance for the other classes. We find that two-phase training based on majority undersampling increases class-specific F1-scores up to 3.0%. We also find that two-phase training outperforms using only oversampling or undersampling by 6.1% in F1-score on average. Finally, we find that a combination of over- and undersampling leads to a better performance than using them individually. Authors: Farjad Malik (KU Leuven); Simon Wouters (KU Leuven); Ruben Cartuyvels (KULeuven); Erfan Ghadery (KU Leuven); Sien Moens (KU Leuven)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2021	Capturing Electricity Market Dynamics in the Optimal Trading of Strategic Agents using Neural Network Constrained Optimization (Papers Track) Abstract and authors: (click to expand) Abstract: In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporate complex market dynamics in the optimal bidding strategy. This method uses input-convex neural networks (ICNNs) to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., to replace the lower-level problem by a neural network. In a comparative analysis, the optimal bidding problem of a load agent is formulated via the proposed MPNNC and via the classical bi-level programming method, and compared against each other. Authors: Mihály Dolányi (KU Leuven); Kenneth Bruninx (KU Leuven); Jean-François Toubeau (Faculté Polytechnique (FPMs), Université de Mons (UMONS)); Erik Delaue (KU Leuven)	Hybrid Physical Models Climate Finance & Economics Recommender Systems
NeurIPS 2021	Leveraging Machine Learning to Predict the Autoconversion Rates from Satellite Data (Papers Track) Abstract and authors: (click to expand) Abstract: One way of reducing the uncertainty involved in determining the radiative forcing of climate change is by understanding the interaction between aerosols, clouds, and precipitation processes. This can be studied using high-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM). However, due to the extremely high computational cost required, this simulation-based approach can only be run for a limited amount of time within a limited area. To address this, we developed new models using emerging machine learning approaches that leverage a plethora of satellite observations providing long-term global spatial coverage. In particular, our machine learning models are capable of capturing the key process of precipitation formation which greatly control cloud lifetime, namely autoconversion rates -- the term used to describe the collision and coalescence of cloud droplets responsible for raindrop formation. We validate the performance of our models against simulation data, showing that our models are capable of predicting the autoconversion rates fairly well. Authors: Maria C Novitasari (University College London); Johannes Quaas (University of Leipzig); Miguel Rodrigues (University College London)	Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2021	Towards Automatic Transformer-based Cloud Classification and Segmentation (Papers Track) Abstract and authors: (click to expand) Abstract: Clouds have been demonstrated to have a huge impact on the energy balance, temperature, and weather of the Earth. Classification and segmentation of clouds and coverage factors is crucial for climate modelling, meteorological studies, solar energy industry, and satellite communication. For example, clouds have a tremendous impact on short-term predictions or 'nowcasts' of solar irradiance and can be used to optimize solar power plants and effectively exploit solar energy. However even today, cloud observation requires the intervention of highly-trained professionals to document their findings, which introduces bias. To overcome these issues and contribute to climate change technology, we propose, to the best of our knowledge, the first two transformer-based models applied to cloud data tasks. We use the CCSD Cloud classification dataset and achieve 90.06% accuracy, outperforming all other methods. To demonstrate the robustness of transformers in this domain, we perform Cloud segmentation on SWIMSWG dataset and achieve 83.2% IoU, also outperforming other methods. With this, we signal a potential shift away from pure CNN networks. Authors: Roshan Roy (Birla Institute of Technology and Science, Pilani); Ahan M R (BITS Pilani); Vaibhav Soni (MANIT Bhopal); Ashish Chittora (BITS Pilani)	Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring Data Mining
NeurIPS 2021	Scalable coastal inundation mapping using machine learning (Papers Track) Abstract and authors: (click to expand) Abstract: Coastal flooding is a significant climate hazard with impacts across economic sectors and society. This study provides a proof of concept for data-driven models for coastal flood inundation at the country scale, incorporating storm dynamics and geospatial characteristics to improve upon simpler geomorphological models. The best fit machine learning model scores an AUC of 0.92 in predicting flooded locations. For a case study storm event in December 2013 we find that all models over-predict flood extents, but that the machine learning model extents were closest to those observed. Authors: Ophelie Meuriot (IBM Research Europe); Anne Jones (IBM Research)	Climate Science & Modeling
NeurIPS 2021	Machine Learning in Automating Carbon Sequestration Site Assessment (Proposals Track) Abstract and authors: (click to expand) Abstract: Carbon capture and sequestration are viewed as an indispensable component to achieve the Paris Agreement climate goal, i.e., keep the global warming within 2 degrees Celsius from pre-industrial levels. Once captured, most CO2 needs to be stored securely for at least decades, preferably in deep underground geological formations. It is economical to inject and store CO2 near/around a depleted gas/oil reservoir or well, where a geological trap for CO2 with good sealing properties and some minimum infrastructure exist. In this proposal, with our preliminary work, it is shown that Machine Learning tools like Optical Character Recognition and Natural Language Processing can aid in screening and selection of injection sites for CO2 storage, facilitate identification of possible CO2 leakage paths in the subsurface, and assist in locating a depleted gas/oil well suitable for CO2 injection and long-term storage. The automated process based on ML tools can also drastically decrease the decision-making cycle time in site selection and assessment phase by reducing human effort. In the longer term, we expect ML tools like Deep Neural Networks to be utilized in CO2 storage monitoring, injection optimization etc. By injecting CO2 into a trapping geological underground formation in a safe and sustainable manner, the Energy industry can contribute substantially to reducing global warming and achieving the goals of the Paris Agreement by the end of this century. Authors: Jay Chen (Shell); Ligang Lu (Shell); Mohamed Sidahmed (Shell); Taixu Bai (Shell); Ilyana Folmar (Shell); Puneet Seth (Shell); Manoj Sarfare (Shell); Duane Mikulencak (Shell); Ihab Akil (Shell)	Carbon Capture & Sequestration Climate Finance & Economics Active Learning Computer Vision & Remote Sensing
NeurIPS 2021	A Risk Model for Predicting Powerline-induced Wildfires in Distribution System (Proposals Track) Abstract and authors: (click to expand) Abstract: The power grid is one of the most common causes of wildfires that result in tremendous economic loss and significant life risk. In this study, we propose to use machine learning techniques to build a risk model for predicting powerline-induced wildfires in distribution system. We collect weather, vegetation, and infrastructure data for all feeders in Pacific Gas & Electricity territory. This study will contribute to a deeper understanding of powerline-induced wildfire prediction and provide valuable suggestions for wildfire mitigation planning. Authors: Mengqi Yao (University of California Berkeley)	Disaster Management and Relief Power & Energy Data Mining
NeurIPS 2021	Detecting Abandoned Oil And Gas Wells Using Machine Learning And Semantic Segmentation (Proposals Track) Abstract and authors: (click to expand) Abstract: Around the world, there are millions of unplugged abandoned oil and gas wells, leaking methane into the atmosphere. The locations of many of these wells, as well as their greenhouse gas emissions impacts, are unknown. Machine learning methods in computer vision and remote sensing, such as semantic segmentation, have made it possible to quickly analyze large amounts of satellite imagery to detect salient information. This project aims to automatically identify undocumented oil and gas wells in the province of Alberta, Canada to aid in documentation, estimation of emissions and maintenance of high-emitting wells. Authors: Michelle Lin (McGill University); David Rolnick (McGill University, Mila)	Power & Energy Computer Vision & Remote Sensing
NeurIPS 2021	Machine learning-enabled model-data integration for predicting subsurface water storage (Proposals Track) Abstract and authors: (click to expand) Abstract: Subsurface water storage (SWS) is a key variable of the climate system and a storage component for precipitation and radiation anomalies, inducing persistence in the climate system. It plays a critical role in climate-change projections and can mitigate the impacts of climate change on ecosystems. However, because of the difficult accessibility of the underground, hydrologic properties and dynamics of SWS are poorly known. Direct observations of SWS are limited, and accurate incorporation of SWS dynamics into Earth system land models remains challenging. We propose a machine learning-enabled model-data integration framework to improve the SWS prediction at local to conus scales in a changing climate by leveraging all the available observation and simulation resources, as well as to inform the model development and guide the observation collection. The accurate prediction will enable an optimal decision of water management and land use and improve the ecosystem's resilience to the climate change. Authors: Dan Lu (Oak Ridge National Laboratory); Eric Pierce (Oak Ridge National Laboratory); Shih-Chieh Kao (Oak Ridge National Laboratory); David Womble (Oak Ridge National Laboratory); LI LI (Pennsylvania State University); Daniella Rempe (The University of Texas at Austin)	Ecosystems & Biodiversity Earth Observation & Monitoring
NeurIPS 2021	Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis (Proposals Track) Abstract and authors: (click to expand) Abstract: Advancing lithium-ion batteries (LIBs) in both design and usage is key to promoting electrification in the coming decades to mitigate human-caused climate change. Inadequate understanding of LIB degradation is an important bottleneck that limits battery durability and safety. Here, we propose hybrid physics-based and data-driven modeling for online diagnosis and prognosis of battery degradation. Compared to existing battery modeling efforts, we aim to build a model with physics as its backbone and statistical learning techniques as enhancements. Such a hybrid model has better generalizability and interpretability together with a well-calibrated uncertainty associated with its prediction, rendering it more valuable and relevant to safety-critical applications under realistic usage scenarios. Authors: Jing Lin (Institute for Infocomm Research); Yu Zhang (I2R); Edwin Khoo (Institute for Infocomm Research)	Hybrid Physical Models Power & Energy Causal & Bayesian Methods Time-series Analysis Uncertainty Quantification & Robustness
NeurIPS 2021	On the use of Deep Generative Models for "Perfect" Prognosis Climate Downscaling (Proposals Track) Abstract and authors: (click to expand) Abstract: Deep Learning has recently emerged as a "perfect" prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising results to reproduce the observed local variability, they are based on the estimation of independent distributions at each location, which leads to deficient spatial structures, especially when downscaling precipitation. This study proposes the use of generative models to improve the spatial consistency of the high-resolution fields, very demanded by some sectoral applications (e.g., hydrology) to tackle climate change. Authors: Jose González-Abad (Institute of Physics of Cantabria); Jorge Baño-Medina (Institute of Physics of Cantabria); Ignacio Heredia (Institute of Physics of Cantabria)	Climate Science & Modeling Generative Modeling
NeurIPS 2021	A Deep Learning application towards transparent communication for Payment for Forest Environmental Services (PES) (Proposals Track) Abstract and authors: (click to expand) Abstract: Deforestation accounts for more than 20% of global emission. Payments for Environmental Services (PES) is seen by both policy makers and practitioners as an effective market-based instrument to provide financial incentives for forest owners, particularly poor and indigenous households in developing countries. It is a critical instrument to protect forests, and ultimately to mitigate climate change and reduce emission from deforestation. However, previous studies have pointed out a key challenge for PES is to ensure transparent payment to local people, due to i) weak monitoring and evaluation and ii) indigenous inaccessibility to e-banking and complying with procedural and administrative paper works to receive payments. Specifically, the amount and the complexity of forms along with the language barriers is a key issue; and most transactions need several intermediaries and transaction costs which reduce the payments reaching landowners. To address these issues, we propose a communication platform that links across the stakeholders and processes. Our proposal will utilize Machine Learning techniques to lower the language barrier and provide technology solutions to help indigenous people to access payments. This would also help improve the effectiveness and transparency of PES schemes. Specifically, we propose the use of Natural Language Processing techniques in providing a speech-to-text and auto translation capability, and the use of Graph Neural Network to provide link predictions of transaction types, volumes and values. The pathway to impact will be forest protection and local livelihood through providing financial incentives, and subsequently contribution to more carbon sequestration and storage – a key issue in climate change mitigation. Authors: Lan HOANG (IBM Research); Thuy Thu Phan (Center for International Forestry Research (CIFOR))	Natural Language Processing Forests
NeurIPS 2021	A NLP-based Analysis of Alignment of Organizations' Climate-Related Risk Disclosures with Material Risks and Metrics (Proposals Track) Abstract and authors: (click to expand) Abstract: The Sustainability Accounting Standards Board (SASB) establishes standards to guide the disclosures of material sustainability and ESG (Environment, Social, Governance)-related information across industries. The availability of quality, comparable and decision-useful information is required to assess risks and opportunities later integrated into financial decision-making. Particularly, standardized, industry-specific climate risk metrics and topics can support these efforts. SASB’s latest climate risk technical bulletin introduces three climate-related risks that are financially material - physical, transition and regulatory risks - and maps these across industries. The main objective of this work is to create a framework that can analyze climate related risk disclosures using an AI-based tool that automatically extracts and categorizes climate-related risks and related metrics from company disclosures based on SASB’s latest climate risk guidance. This process will help with automating large-scale analysis and add much-needed transparency vis-a-vis the current state of climate-related disclosures, while also assessing how far along companies are currently disclosing information on climate risks relevant to their industry. As it stands, this much needed type of analysis is made mostly manually or using third-party metrics, often opaque and biased, as proxies. In this work, we will first create a climate risk glossary that will be trained on a large amount of climate risk text. By combining climate risk keywords in this glossary with recent advances in natural language processing (NLP), we will then be able to quantitatively and qualitatively compare climate risk information in different sectors and industries using a novel climate risk score that will be based on SASB standards. Authors: Elham Kheradmand (University of Montreal); Didier Serre (Clearsum); Manuel Morales (University of Montreal); Cedric B Robert (Clearsum)	Climate Finance & Economics Natural Language Processing
NeurIPS 2021	Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks (Proposals Track) Abstract and authors: (click to expand) Abstract: Methane is one of the most potent greenhouse gases, with the global oil and gas industry being the second largest source of anthropogenic methane emissions, accounting for about 63% of the whole energy sector. This underscores the importance of detecting and remediating methane leaks for the entire oil and gas value chain. Methane sensor networks are a promising technology to detect methane leaks in a timely manner. While they provide near-real-time monitoring of an area of interest, the density of the network can be cost prohibitive, and the identification of the source of the leak is not apparent, especially where there could be more than one source. To address these issues, we developed a machine learning framework that leverages various data sources including oil and gas facilities data, historical methane leak rate distribution and meteorological data, to optimize sensor placement. The determination of sensor locations follows the objective to maximize the detection of possible methane leaks with a limited sensor budget. Authors: Shirui Wang (University of Houston); Sara Malvar (Microsoft); Leonardo Nunes (Microsoft); Kim Whitehall (Microsoft); YAGNA DEEPIKA ORUGANTI (MICROSOFT); Yazeed Alaudah (Microsoft); Anirudh Badam (Microsoft)	Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2021	Multi-agent reinforcement learning for renewable integration in the electric power grid (Proposals Track) Abstract and authors: (click to expand) Abstract: As part of the fight against climate change, the electric power system is transitioning from fuel-burning generators to renewable sources of power like wind and solar. To allow for the grid to rely heavily on renewables, important operational changes must be done. For example, novel approaches for frequency regulation, i.e., for balancing in real-time demand and generation, are required to ensure the stability of a renewable electric system. Demand response programs in which loads adjust in part their power consumption for the grid's benefit, can be used to provide frequency regulation. In this proposal, we present and motivate a collaborative multi-agent reinforcement learning approach to meet the algorithmic requirements for providing real-time power balancing with demand response. Authors: Vincent Mai (Mila, Université de Montréal); Tianyu Zhang (Mila, Université de Montréal); Antoine Lesage-Landry (Polytechnique Montréal & GERAD)	Power & Energy Reinforcement Learning
NeurIPS 2021	Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate change poses serious challenges to achieving food security in a time of a need to produce more food to keep up with the world’s increasing demand for food. There is an urgent need to speed up the development of new high yielding varieties with traits of adaptation and mitigation to climate change. Mathematical approaches, including ML approaches, have been used to search for such traits, leading to unprecedented results as some of the traits, including heat traits that have been long sought-for, have been found within a short period of time. Authors: Abdallah Bari (OperAI Canada - Operational AI); Hassan Ouabbou (INRA); Abderrazek Jilal (INRA); Frederick Stoddard (University of Helsinki); Mikko Sillanpää (University of Oulu); Hamid Khazaei (World Vegetable Center)	Carbon Capture & Sequestration Causal & Bayesian Methods Time-series Analysis Unsupervised & Semi-Supervised Learning Agriculture & Food
NeurIPS 2021	Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark (Proposals Track) Abstract and authors: (click to expand) Abstract: Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks. Such models, recently coined as foundation models, have been transformational to the field of natural language processing. While similar models have also been trained on large corpuses of images, they are not well suited for remote sensing data. To stimulate the development of foundation models for Earth monitoring, we propose to develop a new benchmark comprised of a variety of downstream tasks related to climate change. We believe that this can lead to substantial improvements in many existing applications and facilitate the development of new applications. This proposal is also a call for collaboration with the aim of developing a better evaluation process to mitigate potential downsides of foundation models for Earth monitoring. Authors: Alexandre Lacoste (ServiceNow); Evan D Sherwin (Stanford University, Energy and Resources Engineering); Hannah R Kerner (University of Maryland); Hamed Alemohammad (Radiant Earth Foundation); Björn Lütjens (MIT); Jeremy A Irvin (Stanford); David Dao (ETH Zurich); Alex Chang (Service Now); Mehmet Gunturkun (Element Ai); Alexandre Drouin (ServiceNow); Pau Rodriguez (Element AI); David Vazquez (ServiceNow)	Computer Vision & Remote Sensing Climate Science & Modeling Disaster Management and Relief Ecosystems & Biodiversity Meta- and Transfer Learning Land Use
NeurIPS 2021	Optimization of Agricultural Management for Soil Carbon Sequestration based on Deep Reinforcement Learning and Large-Scale Simulations (Proposals Track) Abstract and authors: (click to expand) Abstract: Soil carbon sequestration in croplands has tremendous potential to help mitigate climate change; however, it is challenging to develop the optimal management practices for maximization of the sequestered carbon as well as the crop yield. This project aims to develop an intelligent agricultural management system using deep reinforcement learning (RL) and large-scale soil and crop simulations. To achieve this, we propose to build a simulator to model and simulate the complex soil-water-plant-atmosphere interaction. By formulating the management decision as an RL problem, we can leverage the state-of-the-art algorithms to train management policies through extensive interactions with the simulated environment. The trained policies are expected to maximize the stored organic carbon while maximizing the crop yield in the presence of uncertain weather conditions. The whole system will be tested using data of soil and crops in both mid-west of the United States and the central region of Portugal. The proposed research will impact food security and climate change, two of the most significant challenges currently facing humanity. Authors: Jing Wu (University of Illinois Urbana-Champaign); Pan Zhao (University of Illinois Urbana-Champaign); Ran Tao (University of Illinois Urbana-Champaign); Naira Hovakimyan (UIUC); Guillermo Marcillo (University of Illinois at Urbana-Champaign); Nicolas Martin (University of Illinois at Urbana-Champaign); Carla Ferreira (Royal Institute of Technology); Zahra Kalantari (Royal Institute of Technology); Jennifer Hobbs (IntelinAir Inc.)	Reinforcement Learning Carbon Capture & Sequestration
NeurIPS 2021	Leveraging machine learning for identify hydrological extreme events under global climate change (Proposals Track) Abstract and authors: (click to expand) Abstract: Hydrological extreme events, such as droughts and floods, are highly destructive natural disasters and its occurrence is expected to increase under the future climate change. Accurate and efficient approach to detect such events will provide timely information to assist management strategies for minimizing socio-economic damages. Despite the threshold approach has established to detect extreme events, the missing data from hydroclimate data and accurately identifying these events are still major challenges. The advent of machine learning models can help to identify the occurrence of droughts and floods events accurately and efficiently. Therefore, this proposed study will develop a machine learning model with semi-supervised anomaly detection approach to identify hydrological extreme events with ground-based data. As a test case, we will use 45-years record of hydroclimate data in coastal California, where was the driest region in 2012-2015, following with flash floods events. The expected results will increase communities’ awareness for hydrological extreme events and enable environmental planning and resource management under climate change Authors: Ying-Jung C Deweese (Georgia Insititute of Technology)	Earth Observation & Monitoring
NeurIPS 2021	Predicting Cascading Failures in Power Systems using Graph Convolutional Networks (Proposals Track) Abstract and authors: (click to expand) Abstract: Worldwide targets are set for the increase of renewable power generation in electricity networks on the way to combat climate change. Consequently, a secure power system that can handle the complexities resulted from the increased renewable power integration is crucial. One particular complexity is the possibility of cascading failures — a quick succession of multiple component failures that takes down the system and might also lead to a blackout. Viewing the prediction of cascading failures as a binary classification task, we explore the efficacy of Graph Convolution Networks (GCNs), to detect the early onset of a cascading failure. We perform experiments based on simulated data from a benchmark IEEE test system. Our preliminary findings show that GCNs achieve higher accuracy scores than other baselines which bodes well for detecting cascading failures. It also motivates a more comprehensive study of graph-based deep learning techniques for the current problem. Authors: Tabia Ahmad (University of Strathclyde); Yongli Zhu (Texas A&M Universersity); Panagiotis Papadopoulos (University of Strathclyde)	Power & Energy Carbon Capture & Sequestration Data Mining Unsupervised & Semi-Supervised Learning
NeurIPS 2021	DeepQuake: Artificial Intelligence for Earthquake Forecasting Using Fine-Grained Climate Data (Proposals Track) Best Paper: Proposals Abstract and authors: (click to expand) Abstract: Earthquakes are one of the most catastrophic natural disasters, making accurate, fine-grained, and real-time earthquake forecasting extremely important for the safety and security of human lives. In this work, we propose DeepQuake, a hybrid physics and deep learning model for fine-grained earthquake forecasting using time-series data of the horizontal displacement of earth’s surface measured from continuously operating Global Positioning System (cGPS) data. Recent studies using cGPS data have established a link between transient deformation within earth's crust to climate variables. DeepQuake’s physics-based pre-processing algorithm extracts relevant features including the x, y, and xy components of strain in earth’s crust, capturing earth’s elastic response to these climate variables, and feeds it into a deep learning neural network to predict key earthquake variables such as the time, location, magnitude, and depth of a future earthquake. Results across California show promising correlations between cGPS derived strain patterns and the earthquake catalog ground truth for a given location and time. Authors: Yash Narayan (The Nueva School)	Time-series Analysis Climate Science & Modeling Earth Observation & Monitoring
NeurIPS 2021	A day in a sustainable life (Tutorials Track) Abstract and authors: (click to expand) Abstract: In this notebook, we show the reader how to use an electrical battery to minimize the operational carbon intensity of a building. The central idea is to charge the battery when the carbon intensity of the grid energy mix is low, and vice versa. The same methodology is used in practice to optimise for a number of different objective functions, including energy costs. Taking the hypothetical case of Pi, an eco-conscious and tech-savvy householder in the UK, we walk the reader through getting carbon intensity data, and how to use this with a number of different optimisation algorithms to decarbonise. Starting off with easy-to-understand, brute force search, we establish a baseline for subsequent (hopefully smarter) optimization algorithms. This should come naturally, since in their day job Pi is a data scientist where they often use grid and random search to tune hyperparameters of ML models. The second optimization algorithm we explore is a genetic algorithm, which belongs to the class of derivative free optimizers and is consequently extremely versatile. However, the flexibility of these algorithms comes at the cost of computational speed and effort. In many situations, it makes sense to utilize an optimization method which can make use of the special structure in the problem. As the final step, we see how Pi can optimally solve the problem of minimizing their carbon intensity by formulating it as a linear program. Along the way, we also keep an eye out for some of the most important challenges that arise in practice. Authors: Hussain Kazmi (KU Leuven); Attila Balint (KU Leuven); Jolien Despeghel (KU Leuven)	Buildings Power & Energy Time-series Analysis
NeurIPS 2021	Open Catalyst Project: An Introduction to ML applied to Molecular Simulations (Tutorials Track) Abstract and authors: (click to expand) Abstract: As the world continues to battle energy scarcity and climate change, the future of our energy infrastructure is a growing challenge. Renewable energy technologies offer the opportunity to drive efficient carbon-neutral means for energy storage and generation. Doing so, however, requires the discovery of efficient and economic catalysts (materials) to accelerate associated chemical processes. A common approach in discovering high performance catalysts is using molecular simulations. Specifically, each simulation models the interaction of a catalyst surface with molecules that are commonly seen in electrochemical reactions. By predicting these interactions accurately, the catalyst's impact on the overall rate of a chemical reaction may be estimated. The Open Catalyst Project (OCP) aims to develop new ML methods and models to accelerate the catalyst simulation process for renewable energy technologies and improve our ability to predict properties across catalyst composition. The initial release of the Open Catalyst 2020 (OC20) dataset presented the largest open dataset of molecular combinations, spanning 55 unique elements and over 130M+ data points. We will present a comprehensive tutorial of the Open Catalyst Project repository, including (1) Accessing & visualizing the dataset, (2) Overview of the various tasks, (3) Training graph neural network (GNN) models, (4) Developing your own model for OCP, (5) Running ML-driven simulations, and (6) Visualizing the results. Primary tools include PyTorch and PyTorch Geometric. No background in chemistry is assumed. Following this tutorial we hope to better equip attendees with a basic understanding of the data and repository. Authors: Muhammed Shuaibi (Carnegie Mellon University); Anuroop Sriram (Facebook); Abhishek Das (Facebook AI Research); Janice Lan (Facebook AI Research); Adeesh Kolluru (Carnegie Mellon University); Brandon Wood (NERSC); Zachary Ulissi (Carnegie Mellon University); Larry Zitnick (Facebook AI Research)	Carbon Capture & Sequestration Power & Energy
ICML 2021	Examining the nexus of environmental policy, climate physics, and maritime shipping with deep learning models and space-borne data (Papers Track) Abstract and authors: (click to expand) Abstract: Ship-tracks are produced by ship exhaust interacting with marine low clouds. They provide an ideal lab for constraining a critical climate forcing. However, no global survey of ship ship-tracks has been made since its discovery 55 years ago, which limits research progress. Here we present the first global map of ship-tracks produced by applying deep segmentation models to large satellite data. Our model generalizes well and is validated against independent data. Large-scale ship-track data are at the nexus of environmental policy, climate physics, and maritime shipping industry: they can be used to study aerosol-cloud interactions, the largest uncertainty source in climate forcing; to evaluate compliance and impacts of environmental policies; and to study the impact of significant socioeconomic events on maritime shipping. Based on twenty years of global data, we show cloud physics responses in ship-tracks strongly depend on the cloud regime. Inter-annual fluctuation in ship-track frequency clearly reflects international trade/economic trends. Emission policies strongly affect the pattern of shipping routes and ship-track occurrence. The combination of stricter fuel standard and the COVID-19 pandemic pushed global ship-track frequency to the lowest level in the record. More applications of our technique and data are envisioned such as detecting illicit shipping activity and checking policy compliance of individual ships. Authors: Tianle Yuan (University of Maryland, NASA); Hua Song (NASA, SSAI); Chenxi Wang (University of Maryland, NASA); Kerry Meyer (NASA); Siobhan Light (University of Maryland); Sophia von Hippel (University of Arizona); Steven Platnick (NASA); Lazaros Oreopoulos (NASA); Robert Wood (University of Washington); Hans Mohrmann (University of Washington)	Computer Vision & Remote Sensing Climate Policy Meta- and Transfer Learning
ICML 2021	A human-labeled Landsat-8 contrails dataset (Papers Track) Abstract and authors: (click to expand) Abstract: Contrails (condensation trails) are the ice clouds that trail behind aircraft as they fly through cold and moist regions of the atmosphere. Avoiding these regions could potentially be an inexpensive way to reduce over half of aviation's impact on global warming. Development and evaluation of these avoidance strategies greatly benefits from the ability to detect contrails on satellite imagery. Since little to no public data is available to develop such contrail detectors, we construct and release a dataset of several thousand Landsat-8 scenes with pixel-level annotations of contrails. The dataset will continue to grow, but currently contains 4289 scenes (of which 47% have at least one contrail) representing 950+ person-hours of labeling time. Authors: Kevin McCloskey (Google); Scott Geraedts (Google); Brendan Jackman (Google); Vincent R. Meijer (Laboratory for Aviation and the Environment, Massachusetts Institute of Technology); Erica Brand (Google); Dave Fork (Google); John C. Platt (Google); Carl Elkin (Google); Christopher Van Arsdale (Google)	Computer Vision & Remote Sensing Climate Science & Modeling
ICML 2021	Urban Tree Species Classification Using Aerial Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Urban trees help regulate temperature, reduce energy consumption, improve urban air quality, reduce wind speeds, and mitigating the urban heat island effect. Urban trees also play a key role in climate change mitigation and global warming by capturing and storing atmospheric carbon-dioxide which is the largest contributor to greenhouse gases. Automated tree detection and species classification using aerial imagery can be a powerful tool for sustainable forest and urban tree management. Hence, This study first offers a pipeline for generating labelled dataset of urban trees using Google Map's aerial images and then investigates how state of the art deep Convolutional Neural Network models such as VGG and ResNet handle the classification problem of urban tree aerial images under different parameters. Experimental results show our best model achieves an average accuracy of 60% over 6 tree species. Authors: Emily Waters (Anglia Ruskin University); Mahdi Maktabdar Oghaz (Anglia Ruskin University); Lakshmi Babu Saheer (Anglia Ruskin University)	Carbon Capture & Sequestration Buildings Climate Science & Modeling Computer Vision & Remote Sensing Forests Land Use
ICML 2021	Estimation of Corporate Greenhouse Gas Emissions via Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For these and other international measures to be successful, reliable data is key. The ability to see the carbon footprint of companies around the world will be critical for investors to comply with the measures and hit climate neutrality. However, with only a small portion of companies volunteering to disclose their greenhouse gas (GHG) emissions, it is nearly impossible for investors to align their investment strategies with the measures. By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions. In this paper, we show that our model provides accurate estimates of corporate GHG emissions to investors such that they are able to align their investments with the regulatory measures and achieve net-zero goals. Authors: You Han (Bloomberg L.P.); Achintya Gopal (Bloomberg LP); Liwen Ouyang (Bloomberg L.P.); Aaron Key (Bloomberg LP)	Climate Finance & Economics Heavy Industry and Manufacturing Climate Policy Generative Modeling Uncertainty Quantification & Robustness Unsupervised & Semi-Supervised Learning
ICML 2021	ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins (Papers Track) Abstract and authors: (click to expand) Abstract: Physics-informed dynamical system models form critical components of digital twins of the built environment. These digital twins enable the design of energy-efficient infrastructure, but must be properly calibrated to accurately reflect system behavior for downstream prediction and analysis. Dynamical system models of modern buildings are typically described by a large number of parameters and incur significant computational expenditure during simulations. To handle large-scale calibration of digital twins without exorbitant simulations, we propose ANP-BBO: a scalable and parallelizable batch-wise Bayesian optimization (BBO) methodology that leverages attentive neural processes (ANPs). Authors: Ankush Chakrabarty (Mitsubishi Electric Research Labs); Gordon Wichern (Mitsubishi Electric Research Laboratories (MERL)); Christopher Laughman (Mitsubishi Electric Research Laboratories (MERL))	Buildings Causal & Bayesian Methods
ICML 2021	Seasonal Sea Ice Presence Forecasting of Hudson Bay using Seq2Seq Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate and timely forecasts of sea ice conditions are crucial for safe shipping operations in the Canadian Arctic and other ice-infested waters. Given the advancement of machine-learning methods and the recent observations on the declining trend of Arctic sea ice extent over the past decades due to global warming, new machine learning approaches are deployed to provide additional sea ice forecasting products. This study is novel in comparison with previous machine learning (ML) approaches in the sea-ice forecasting domain as it provides a daily spatial map of probability of ice presence in the domain up to 90 days. The predictions are further used to predict freeze-up/breakup dates and show their capability to capture both the variability and the increasing trend of open water season in the domain over the past decades. Authors: Nazanin Asadi (University of Waterloo); K Andrea Scott (University of Waterloo); Philippe Lamontagne (National Research Council Canada)	Climate Science & Modeling Computer Vision & Remote Sensing Time-series Analysis
ICML 2021	Semantic Segmentation on Unbalanced Remote Sensing Classes for Active Fire (Papers Track) Abstract and authors: (click to expand) Abstract: Wildfires generate considerable research interest due to their high frequency of occurrence along with global climate change. Future wildfire detection sensors would equip an on-orbit processing module that filters the useless raw images before data transmission. To efficiently detect heat anomalies from the single large scene, we need to handle the unbalanced sample sets between small active fire pixels and large-size complex background information. In this study, we contribute to solving this problem by enhancing the target feature representation in three ways. We first preprocess training images by constraining sampling ranges and removing background patches. Then we use the object-contextual representation (OCR) module to strengthen the active fire pixel representation based on the self-attention unit. The HRNet backbone provides multi-scale pixel representation as input to the OCR module. Finally, the combined loss of weighted cross-entropy loss and Lovasz hinge loss improve the segmentation accuracy further by optimizing the IoU of the foreground class. The performance is tested on the aerial FLAME dataset, whose ratio between labeled active fire and background pixels is 5.6%. The proposed framework improves the mIoU from 83.10% (baseline U-Net) to 90.81%. Future research will expand the technique for active fire detection using satellite images. Authors: Xikun Hu (KTH Royal Institute of Technology); Alberto Costa Nogueira Junior (IBM Research Brazil); Tian Jin (College of Electronic Science, National University of Defense Technology)	Disaster Management and Relief Computer Vision & Remote Sensing
ICML 2021	Improving Image-Based Characterization of Porous Media with Deep Generative Models (Papers Track) Abstract and authors: (click to expand) Abstract: Micro- and nanoscale imaging are important for characterizing subsurface formations for carbon sequestration, shale gas recovery, and hydrogen storage. Common imaging techniques, however, are often sample-destructive, expensive, require high levels of expertise, or only acquire planar data. The resulting image datasets therefore may not allow for a representative estimation of rock properties. In this work, we address these challenges in image-based characterization of porous media using deep generative models. We present a machine learning workflow for characterizing porous media from limited imaging data. We develop methods for 3D image volume translation and synthesis from 2D training data, apply this method to grayscale and multimodal image datasets of sandstones and shales, and simulate flow through the generated volumes. Results show that the proposed image reconstruction and generation approaches produce realistic pore-scale 3D representations of rock samples using only 2D training data. The models proposed here expand our capabilities for characterization of rock samples and enable new understanding of pore-scale storage and recovery processes. Authors: Timothy Anderson (Stanford University); Kelly Guan (Stanford University); Bolivia Vega (Stanford University); Laura Froute (Stanford University); Anthony Kovscek (Stanford University)	Generative Modeling Carbon Capture & Sequestration
ICML 2021	Forest Terrain Identification using Semantic Segmentation on UAV Images (Papers Track) Abstract and authors: (click to expand) Abstract: Beavers' habitat is known to alter the terrain, providing biodiversity in the area, and recently their lifestyle is linked to climatic changes by reducing greenhouse gases levels in the region. To analyse the impact of beavers’ habitat on the region, it is, therefore, necessary to estimate the terrain alterations caused by beaver actions. Furthermore, such terrain analysis can also play an important role in domains like wildlife ecology, deforestation, land-cover estimations, and geological mapping. Deep learning models are known to provide better estimates on automatic feature identification and classification of a terrain. However, such models require significant training data. Pre-existing terrain datasets (both real and synthetic) like CityScapes, PASCAL, UAVID, etc, are mostly concentrated on urban areas and include roads, pathways, buildings, etc. Such datasets, therefore, are unsuitable for forest terrain analysis. This paper contributes, by providing a finely labelled novel dataset of forest imagery around beavers’ habitat, captured from a high-resolution camera on an aerial drone. The dataset consists of 100 such images labelled and classified based on 9 different classes. Furthermore, a baseline is established on this dataset using state-of-the-art semantic segmentation models based on performance metrics including Intersection Over Union (IoU), Overall Accuracy (OA), and F1 score. Authors: Muhammad Umar (Anglia Ruskin University); Lakshmi Babu Saheer (Anglia Ruskin University); Javad Zarrin (Anglia Ruskin University)	Ecosystems & Biodiversity Computer Vision & Remote Sensing Land Use
ICML 2021	Climate-based ensemble machine learning model to forecast Dengue epidemics (Papers Track) Abstract and authors: (click to expand) Abstract: Dengue fever is one of the most common and rapidly spreading arboviral diseases in the world, with major public health and economic consequences in tropical and sub-tropical regions. Countries such as Peru, 17.143 cases of dengue were reported in 2019, where 81.4% of cases concentrated in five of the 25 departments. When predicting infectious disease outbreaks, it is crucial to model the long-term dependency in time series data. However, this is challenging when performed on a countrywide level since dengue incidence varies across administrative areas. Therefore, this study developed and applied a climate-based ensemble model using multiple machine learning (ML) approaches to forecast dengue incidence rate (DIR) by department. The ensemble combined the outputs from Long Short-Term Memory (LSTM) recurrent neural network and Categorical Boosting (CatBoost) methods to predict DIR one month ahead for each department in Peru. Monthly dengue cases stratified by Peruvian departments were analysed in conjunction with associated demographic, geographic, and satellite-based meteorological data for the period January 2010–December 2019. The results demonstrated that the ensemble model was able to forecast DIR in low-transmission departments, while the model was less able to detect sudden DIR peaks in some departments. Air temperature and wind components demonstrated to be the significant predictors for DIR predictions. This dengue forecast model is timely and can help local governments to implement effective control measures and mitigate the effects of the disease. This study advances the state-of-the-art of climate services for the public health sector, by informing what are the key climate factors responsible for triggering dengue transmission. Finally, this project summarises how important it is to perform collaborative work with complementary expertise from intergovernmental organizations and public health universities to advance knowledge and address societal challenges. Authors: Rochelle Schneider (European Space Agency); Alessandro Sebastianelli (European Space Agency); Dario Spiller (Italian Space Agency); James Wheeler (European Space Agency); Raquel Carmo (European Space Agency); Artur Nowakowski (Warsaw University of Technology); Manuel Garcia-Herranz (UNICEF); Dohyung Kim (UNICEF); Hanoch Barlevi (UNICEF LACRO); Zoraya El Raiss Cordero (UNICEF LACRO); Silvia Liberata Ullo (University of Sannio); Pierre-Philippe Mathieu (European Space Agency); Rachel Lowe (London School of Hygiene & Tropical Medicine)	Societal Adaptation & Resilience Health
ICML 2021	Physics-Informed Graph Neural Networks for Robust Fault Location in Power Grids (Papers Track) Best Paper: ML Innovation Abstract and authors: (click to expand) Abstract: The reducing cost of renewable energy resources, such as solar photovoltaics (PV) and wind farms, is accelerating global energy transformation to mitigate climate change. However, a high level of intermittent renewable energy causes power grids to have more stability issues. This accentuates the need for quick location of system failures and follow-up control actions. In recent events such as in California, line failures have resulted in large-scale wildfires leading to loss of life and property. In this article, we propose a two-stage graph learning framework to locate power grid faults in the challenging but practical regime characterized by (a) sparse observations, (b) low label rates, and (c) system variability. Our approach embeds the geometrical structure of power grids into the graph neural networks (GNN) in stage I for fast fault location, and then stage II further enhances the location accuracy by employing the physical similarity of the labeled and unlabeled data samples. We compare our approach with three baselines in the IEEE 123-node benchmark system and show that it outperforms the others by significant margins in various scenarios. Authors: Wenting Li (Los Alamos National Laboratory); Deepjyoti Deka (Los Alamos National Laboratory)	Power & Energy Hybrid Physical Models Interpretable ML Uncertainty Quantification & Robustness Unsupervised & Semi-Supervised Learning
ICML 2021	Prediction of Boreal Peatland Fires in Canada using Spatio-Temporal Methods (Papers Track) Abstract and authors: (click to expand) Abstract: Peat fires are the largest fires on earth in terms of fuel consumption and are responsible for a significant portion of global carbon emissions. Predicting fires in the peatlands can help decision-makers and researchers monitor and prevent peat fires. Despite this, research on predicting peatland fires remains largely understudied as compared to the prediction of other forms of fires. However, peatland fires are unique among fires and therefore require datasets and architectures attuned to their particular characteristics. In this paper, we present a new dataset, PeatSet, designed specifically for the problem of peatland fire prediction. In addition, we propose several models to tackle the problem of fire prediction for the peatlands. We develop novel neural architectures for peatland fire prediction, PeatNet, and PT-Net, with a graph-based and a transformer-based architecture, respectively. Our results indicate that these new deep-learning architectures outperform a regression baseline from existing peatland research. Among all the tested models, PT-Net achieves the highest F1 score of 0.1006 and an overall accuracy of 99.84%. Authors: Shreya Bali (Carnegie Mellon University); Sydney Zheng (Carnegie Mellon University); Akshina Gupta (Carnegie Mellon University); Yue Wu (None); Blair Chen (Carnegie Mellon University); Anirban Chowdhury (Carnegie Mellon University); Justin Khim (Carnegie Mellon University)	Disaster Management and Relief Climate Science & Modeling
ICML 2021	Revealing the impact of global warming on climate modes using transparent machine learning and a suite of climate models (Papers Track) Abstract and authors: (click to expand) Abstract: The ocean is key to climate through its ability to store and transport heat and carbon. From studies of past climates, it is clear that the ocean can exhibit a range of dramatic variability that could have catastrophic impacts on society, such as changes in rainfall, severe weather, sea level rise and large scale climate patterns. The mechanisms of change remain obscure, but are explored using a transparent machine learning method, Tracking global Heating with Ocean Regimes (THOR) presented here. We investigate two future scenarios, one where CO2 is increased by 1% per year, and one where CO2 is abruptly quadrupled. THOR is engineered combining interpretable and explainable methods to reveal its source of predictive skill. At the core of THOR, is the identification of dynamically coherent regimes governing the circulation, a fundamental question within oceanography. Three key regions are investigated here. First, the North Atlantic circulation that delivers heat to the higher latitudes is seen to weaken and we identify associated dynamical changes. Second, the Southern Ocean circulation, the strongest circulation on earth, is seen to intensify where we reveal the implications for interactions with the ice on Antarctica. Third, shifts in ocean circulation regimes are identified in the tropical Pacific region, with potential impacts on the El Nino Southern Oscillation, Earth’s dominant source of year-to-year climate variations affecting weather extremes, ecosystems, agriculture, and fisheries. Together with revealing these climatically relevant ocean dynamics, THOR also constitutes a step towards trustworthy machine learning called for within oceanography and beyond because its predictions are physically tractable. We conclude with by highlighting open questions and potentially fruitful avenues of further machine learning applications to climate research. Authors: Maike Sonnewald (Princeton University); Redouane Lguensat (LSCE-IPSL); Aparna Radhakrishnan (Geophysical Fluid Dynamics Laboratory); Zoubero Sayibou (Bronx Community College); Venkatramani Balaji (Princeton University); Andrew Wittenberg (NOAA)	Climate Science & Modeling Interpretable ML
ICML 2021	Challenges in Applying Audio Classification Models to Datasets Containing Crucial Biodiversity Information (Papers Track) Abstract and authors: (click to expand) Abstract: The acoustic signature of a natural soundscape can reveal consequences of climate change on biodiversity. Hardware costs, human labor time, and expertise dedicated to labeling audio are impediments to conducting acoustic surveys across a representative portion of an ecosystem. These barriers are quickly eroding away with the advent of low-cost, easy to use, open source hardware and the expansion of the machine learning field providing pre-trained neural networks to test on retrieved acoustic data. One consistent challenge in passive acoustic monitoring (PAM) is a lack of reliability from neural networks on audio recordings collected in the field that contain crucial biodiversity information that otherwise show promising results from publicly available training and test sets. To demonstrate this challenge, we tested a hybrid recurrent neural network (RNN) and convolutional neural network (CNN) binary classifier trained for bird presence/absence on two Peruvian bird audiosets. The RNN achieved an area under the receiver operating characteristics (AUROC) of 95% on a dataset collected from Xeno-canto and Google’s AudioSet ontology in contrast to 65% across a stratified random sample of field recordings collected from the Madre de Dios region of the Peruvian Amazon. In an attempt to alleviate this discrepancy, we applied various audio data augmentation techniques in the network’s training process which led to an AUROC of 77% across the field recordings. Authors: Jacob G Ayers (UC San Diego); Yaman Jandali (University of California, San Diego); Yoo-Jin Hwang (Harvey Mudd College); Erika Joun (University of California, San Diego); Gabriel Steinberg (Binghampton University); Mathias Tobler (San Diego Zoo Wildlife Alliance); Ian Ingram (San Diego Zoo Wildlife Alliance); Ryan Kastner (University of California San Diego); Curt Schurgers (University of California San Diego)	Ecosystems & Biodiversity Natural Language Processing
ICML 2021	Learning Optimal Power Flow with Infeasibility Awareness (Papers Track) Abstract and authors: (click to expand) Abstract: Optimal power flow provides an energy-efficient operating point for power grids and therefore supports climate change mitigation. This function has to be run every few minutes day and night, thus a reliable and computationally efficient solution method is of vital importance. Deep learning seems a promising direction, and related works have emerged recently. However, considering feasible scenarios only during the learning process, existing works will mislead system operators in infeasible scenarios and pose a new threat to system resilience. Paying attention to infeasibility in the decision making process, this paper tackles this emerging threat with multi-task learning. Case studies on the IEEE test system validate the effectiveness of the proposed method. Authors: Gang Huang (Zhejiang Lab); Longfei Liao (Zhejiang Lab); Lechao Cheng (Zhejiang Lab); Wei Hua (Zhejiang Lab)	Power & Energy
ICML 2021	Reconstructing Aerosol Vertical Profiles with Aggregate Output Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of the anthropogenic climate change. This uncertainty arises in part from the inability to observe aerosol amounts at the cloud formation levels, and, more broadly, the vertical distribution of aerosols. Hence, we often have to settle for less informative two-dimensional proxies, i.e. vertically aggregated data. In this work, we formulate the problem of disaggregation of vertical profiles of aerosols. We propose some initial solutions for such aggregate output regression problem and demonstrate their potential on climate model data. Authors: Sofija Stefanovic (University of Oxford); Shahine Bouabid (University of Oxford); Philip Stier (University of Oxford); Athanasios Nenes (EPFL); Dino Sejdinovic (University of Oxford)	Climate Science & Modeling Unsupervised & Semi-Supervised Learning
ICML 2021	Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models (Papers Track) Abstract and authors: (click to expand) Abstract: Ensemble data from Earth system models has to be calibrated and post-processed. I propose a novel member-by-member post-processing approach with neural networks. I bridge ideas from ensemble data assimilation with self-attention, resulting into the self-attentive ensemble transformer. Here, interactions between ensemble members are represented as additive and dynamic self-attentive part. As proof-of-concept, I regress global ECMWF ensemble forecasts to 2-metre-temperature fields from the ERA5 reanalysis. I demonstrate that the ensemble transformer can calibrate the ensemble spread and extract additional information from the ensemble. As it is a member-by-member approach, the ensemble transformer directly outputs multivariate and spatially-coherent ensemble members. Therefore, self-attention and the transformer technique can be a missing piece for a non-parametric post-processing of ensemble data with neural networks. Authors: Tobias S Finn (Universität Hamburg)	Climate Science & Modeling
ICML 2021	DeepOPF-NGT: A Fast Unsupervised Learning Approach for Solving AC-OPF Problems without Ground Truth (Papers Track) Abstract and authors: (click to expand) Abstract: AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain the reliable and cost-effective operation of power systems. Recently, supervised-learning approaches have been developed to speed up the solving time of AC-OPF problems without incurring infeasibility or much optimality loss by learning the load-solution mapping embedded in the training dataset. However, it is non-trivial and computationally expensive to prepare the training dataset with single embedded mapping, due to that AC-OPF problems are non-convex and may admit multiple optimal solutions. In this paper, we develop an unsupervised learning approach (DeepOPF-NGT) for solving AC-OPF problems, which does not require training datasets with ground truth to operate. Instead, it uses a properly designed loss function to guide the tuning of the neural network parameters to directly learn one load-solution mapping. Preliminary results on the IEEE 30-bus test system show that the unsupervised DeepOPF-NGT approach can achieve comparable optimality, feasibility, and speedup performance against an existing supervised learning approach. Authors: Wanjun Huang (City University of Hong Kong); Minghua Chen (City University of Hong Kong)	Power & Energy Unsupervised & Semi-Supervised Learning
ICML 2021	Commercial Vehicle Traffic Detection from Satellite Imagery with Deep Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Road freight traffic is a major greenhouse gas emitter: commercial vehicles (CVs) contribute ∼7% to the global CO 2 emission budget, a fraction that is likely to increase in the future. The quantitative monitoring of CV traffic rates, while essential for the implementation of targeted road emission regulations, is costly and as such only available in developed regions. In this work, we investigate the feasibility of estimating hourly CV traffic rates from freely available Sentinel-2 satellite imagery. We train a modified Faster R-CNN object detection model to detect individual CVs in satellite images and feed the resulting counts into a regression model to predict hourly CV traffic rates. This architecture, when trained on ground-truth data for Switzerland, is able to estimate hourly CV traffic rates for any freeway section within 58% (MAPE) of the actual value; for freeway sections with historic information on CV traffic rates, we can predict hourly CV traffic rates up to within 4% (MAPE). We successfully apply our model to freeway sections in other coun tries and show-case its utility by quantifying the change in traffic patterns as a result of the first CoVID-19 lockdown in Switzerland. Our results show that it is possible to estimate hourly CV traffic rates from satellite images, which can guide civil engineers and policy makers, especially in developing countries, in monitoring and reducing greenhouse gas emissions from CV traffic. Authors: Moritz Blattner (University of St. Gallen); Michael Mommert (University of St. Gallen); Damian Borth (University of St. Gallen)	Computer Vision & Remote Sensing Transportation
ICML 2021	Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows (Papers Track) Abstract and authors: (click to expand) Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus enables various applications in low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein-Polynomial Normalizing Flows where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities and also outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures. Authors: Marcel Arpogaus (Konstanz University of Applied Sciences); Marcus Voß (Technische Universität Berlin (DAI-Labor)); Beate Sick (ZHAW and University of Zurich); Mark Nigge-Uricher (Bosch.IO GmbH); Oliver Dürr (Konstanz University of Applied Sciences)	Power & Energy Buildings Heavy Industry and Manufacturing Generative Modeling Interpretable ML Time-series Analysis Uncertainty Quantification & Robustness
ICML 2021	Guided A* Search for Scheduling Power Generation Under Uncertainty (Papers Track) Abstract and authors: (click to expand) Abstract: Increasing renewables penetration motivates the development of new approaches to operating power systems under uncertainty. We apply a novel approach combining self-play reinforcement learning (RL) and traditional planning to solve the unit commitment problem, an essential power systems scheduling task. Applied to problems with stochastic demand and wind generation, our results show significant cost reductions and improvements to security of supply as compared with an industry-standard mixed-integer linear programming benchmark. Applying a carbon price of \$50/tCO$_2$ achieves carbon emissions reductions of up to 10\%. Our results demonstrate scalability to larger problems than tackled in existing literature, and indicate the potential for RL to contribute to decarbonising power systems. Authors: Patrick de Mars (UCL); Aidan O'Sullivan (UCL)	Power & Energy Reinforcement Learning
ICML 2021	DroughtED: A dataset and methodology for drought forecasting spanning multiple climate zones (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change exacerbates the frequency, duration and extent of extreme weather events such as drought. Previous attempts to forecast drought conditions using machine learning have focused on regional models which have two major limitations for national drought management: (i) they are trained on localised climate data and (ii) their architectures prevent them from being applied to new heterogeneous regions. In this work, we present a new large-scale dataset for training machine learning models to forecast national drought conditions, named DroughtED. The dataset consists of globally available meteorological features widely used for drought prediction, paired with location meta-data which has not previously been utilised for drought forecasting. Here we also establish a baseline on DroughtED and present the first research to apply deep learning models - Long Short-Term Memory (LSTMs) and Transformers - to predict county-level drought conditions across the full extent of the United States. Our results indicate that DroughtED enables deep learning models to learn cross-region patterns in climate data that contribute to drought conditions and models trained on DroughtED compare favourably to state-of-the-art drought prediction models trained on individual regions. Authors: Christoph D Minixhofer (The University of Edinburgh); Mark Swan (The University of Edinburgh); Calum McMeekin (The University of Edinburgh); Pavlos Andreadis (The University of Edinburgh)	Disaster Management and Relief Climate Science & Modeling Data Mining Meta- and Transfer Learning Time-series Analysis
ICML 2021	Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space (Papers Track) Abstract and authors: (click to expand) Abstract: Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite the importance of limiting GHG emissions to mitigate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Existing models for surface-level air pollution rely on extensive land-use datasets which are often locally restricted and temporally static. This work proposes a deep learning approach for the prediction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satellite imagery with satellite-based atmospheric column density air pollution measurements enables the scaling of air pollution estimates (in this case NO2) to high spatial resolution (up to ~10m) at arbitrary locations and adds a temporal component to these estimates. The proposed model performs with high accuracy when evaluated against air quality measurements from ground stations (mean absolute error <6 microgram/m^3). Our results enable the identification and temporal monitoring of major sources of air pollution and GHGs. Authors: Linus M. Scheibenreif (University of St. Gallen); Michael Mommert (University of St. Gallen); Damian Borth (University of St. Gallen)	Computer Vision & Remote Sensing Climate Science & Modeling
ICML 2021	Emulating Aerosol Microphysics with a Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average $R^2$ score of $89\%$. On a GPU we achieve a speed-up of 120 compared to the original model. Authors: Paula Harder (Fraunhofer ITWM); Duncan Watson-Parris (University of Oxford); Dominik Strassel (Fraunhofer ITWM); Nicolas Gauger (TU Kaiserslautern); Philip Stier (University of Oxford); Janis Keuper (hs-offenburg)	Climate Science & Modeling
ICML 2021	Automated Identification of Climate Risk Disclosures in Annual Corporate Reports (Papers Track) Abstract and authors: (click to expand) Abstract: It is important for policymakers to understand which financial policies are effective in increasing climate risk disclosure in corporate reporting. We use machine learning to automatically identify disclosures of five different types of climate-related risks. For this purpose, we have created a dataset of over 120 manually-annotated annual reports by European firms. Applying our approach to reporting of 337 firms over the last 20 years, we find that risk disclosure is increasing. Disclosure of transition risks grows more dynamically than physical risks, and there are marked differences across industries. Country-specific dynamics indicate that regulatory environments potentially have an important role to play for increasing disclosure. Authors: David Friederich (University of Bern); Lynn Kaack (ETH Zurich); Sasha Luccioni (Mila); Bjarne Steffen (ETH Zurich)	Climate Finance & Economics Natural Language Processing
ICML 2021	Forecasting Black Sigatoka Infection Risks with Latent Neural ODEs (Papers Track) Abstract and authors: (click to expand) Abstract: Black Sigatoka is the most widely-distributed and destructive disease affecting banana plants. Due to the heavy financial burden of managing this infectious disease, farmers in developing countries face significant banana crop losses. The spread of black Sigatoka is highly dependent on weather conditions and though scientists have produced mathematical models of infectious diseases, adapting these models to incorporate climate effects is difficult. We present MR. NODE (Multiple predictoR Neural ODE), a neural network that models the dynamics of black Sigatoka infection learnt directly from data via Neural Ordinary Differential Equations. Our method encodes external predictor factors into the latent space in addition to the variable that we infer, and it can also predict the infection risk at an arbitrary point in time. Empirically, we demonstrate on historical climate data that our method has superior generalization performance on time points up to one month in the future and unseen irregularities. We believe that our method can be a useful tool to control the spread of black Sigatoka. Authors: Yuchen Wang (University of Toronto); Matthieu Chan Chee (University of Toronto); Ziyad Edher (University of Toronto); Minh Duc Hoang (University of Toronto); Shion Fujimori (University of Toronto); Jesse Bettencourt (University of Toronto)	Time-series Analysis Agriculture & Food
ICML 2021	A Reinforcement Learning Approach to Home Energy Management for Modulating Heat Pumps and Photovoltaic Systems (Papers Track) Abstract and authors: (click to expand) Abstract: Efficient sector coupling in residential buildings plays a key role in supporting the energy transition. In this study, we analyze the potential of using reinforcement learning (RL) to control a home energy management system. We conduct this study by modeling a representative building with a modulating air-sourced heat pump, a photovoltaic system, a battery, and thermal storage systems for floor heating and hot-water supply. In our numerical analysis, we benchmark our reinforcement learning results using DDPG with the optimal solution generated with model predictive control using a mixed-integer linear model under full information. Our input data, models, and the RL environment, developed using the Julia programming language, will be available in an open-source manner. Authors: Lissy Langer (TU Berlin)	Buildings Reinforcement Learning
ICML 2021	Reinforcement Learning for Optimal Frequency Control: A Lyapunov Approach (Papers Track) Abstract and authors: (click to expand) Abstract: Renewable energy resources play a vital role in reducing carbon emissions and are becoming increasingly common in the grid. On one hand, they are challenging to integrate into a power system because the lack of rotating mechanical inertia can lead to frequency instabilities. On the other hand, these resources have power electronic interfaces that are capable of implementing almost arbitrary control laws. To design these controllers, reinforcement learning has emerged as a popular method to search for policy parameterized by neural networks. The key challenge with learning based approaches is enforcing the constraint that the learned controller need to be stabilizing. Through a Lyapunov function, we explicitly identify the structure of neural network-based controllers such that they guarantee system stability by design. A recurrent RL architecture is used to efficiently train the controllers and they outperform other approaches as demonstrated by simulations. Authors: Wenqi Cui (University of Washington); Baosen Zhang (University of Washington)	Power & Energy Reinforcement Learning
ICML 2021	Modeling Bird Migration by Disaggregating Population Level Observations (Papers Track) Abstract and authors: (click to expand) Abstract: Birds are shifting migratory routes and timing in response to climate change, but modeling migration to better understand these changes is difficult. Some recent work leverages fluid dynamics models, but this requires individual flight speed and directional data which may not be readily available. We developed an alternate modeling method which only requires population level positional data and use it to model migration routes of the American Woodcock (Scolopax minor). We use our model to sample simulated bird trajectories and compare them to real trajectories in order to evaluate the model. Authors: Miguel Fuentes (University of Massachusetts, Amherst); Benjamin Van Doren (Cornell University); Daniel Sheldon (University of Massachusetts, Amherst)	Ecosystems & Biodiversity
ICML 2021	Power Grid Cascading Failure Mitigation by Reinforcement Learning (Papers Track) Abstract and authors: (click to expand) Abstract: This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is formulated, and its challenges are investigated. The problem is then tackled by the RL based on DCOPF (Direct Current Optimal Power Flow). Designs of the RL framework (rewards, states, etc.) are illustrated in detail. Experiments on the IEEE 118-bus system by the proposed RL method demonstrate promising performance in reducing system collapses. Authors: Yongli Zhu (Texas A&M University)	Disaster Management and Relief Power & Energy
ICML 2021	Decadal Forecasts with ResDMD: a residual DMD neural network (Papers Track) Abstract and authors: (click to expand) Abstract: Significant investment is being made by operational forecasting centers to produce decadal (1-10 year) forecasts that can support long-term decision making for a more climate-resilient society. One method that has been employed for this task is the Dynamic Mode Decomposition (DMD) algorithm – also known as the Linear Inverse Model– which is used to fit linear dynamical models to data. While the DMD usually approximates non-linear terms in the true dynamics as a linear system with random noise, we investigate an extension to the DMD to explicitly represent the non-linear terms as a neural network. Our weight initialization allows the network to produce sensible results before training and then improve the prediction after training as data becomes available. In this short paper, we evaluate the proposed architecture for simulating global sea surface temperatures and compare the results with the standard DMD and seasonal forecasts produced by the state-of-the-art dynamical model, CFSv2. Authors: EDUARDO ROCHA RODRIGUES (IBM Research); Campbell Watson (IBM Reserch); Bianca Zadrozny (IBM Research); David Gold (IBM Global Business Services)	Climate Science & Modeling Time-series Analysis
ICML 2021	TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data (Papers Track) Abstract and authors: (click to expand) Abstract: Acquiring a better understanding of drought impacts becomes increasingly vital under a warming climate. Traditional drought indices describe mainly biophysical variables and not impacts on social, economic, and environmental systems. We utilized natural language processing and bidirectional encoder representation from Transformers (BERT) based transfer learning to fine-tune the model on the data from the news-based Drought Impact Report (DIR) and then apply it to recognize seven types of drought impacts based on the filtered Twitter data from the United States. Our model achieved a satisfying macro-F1 score of 0.89 on the DIR test set. The model was then applied to California tweets and validated with keyword-based labels. The macro-F1 score was 0.58. However, due to the limitation of keywords, we also spot-checked tweets with controversial labels. 83.5% of BERT labels were correct compared to the keyword labels. Overall, the fine-tuned BERT-based recognizer provided proper predictions and valuable information on drought impacts. The interpretation and analysis of the model were consistent with experiential domain expertise. Authors: Beichen Zhang (University of Nebraska-Lincoln); Frank Schilder (Thomson Reuters); Kelly Smith (National Drought Mitigation Center); Michael Hayes (University of Nebraska-Lincoln); Sherri Harms (University of Nebraska-Kearney); Tsegaye Tadesse (University of Nebraska-Lincoln)	Disaster Management and Relief Natural Language Processing
ICML 2021	Graph Neural Networks for Learning Real-Time Prices in Electricity Market (Papers Track) Abstract and authors: (click to expand) Abstract: Solving the optimal power flow (OPF) problem in real-time electricity market improves the efficiency and reliability in the integration of low-carbon energy resources into the power grids. To address the scalability and adaptivity issues of existing end-to-end OPF learning solutions, we propose a new graph neural network (GNN) framework for predicting the electricity market prices from solving OPFs. The proposed GNN-for-OPF framework innovatively exploits the locality property of prices and introduces physics-aware regularization, while attaining reduced model complexity and fast adaptivity to varying grid topology. Numerical tests have validated the learning efficiency and adaptivity improvements of our proposed method over existing approaches. Authors: Shaohui Liu (University of Texas at Austin); Chengyang Wu (University of Texas at Austin); Hao Zhu (University of Texas at Austin)	Power & Energy
ICML 2021	Learning Granger Causal Feature Representations (Papers Track) Abstract and authors: (click to expand) Abstract: Tackling climate change needs to understand the complex phenomena occurring on the Planet. Discovering teleconnection patterns is an essential part of the endeavor. Events like El Niño Southern Oscillation (ENSO) impact essential climate variables at large distances, and influence the underlying Earth system dynamics. However, their automatic identification from the wealth of observational data is still unresolved. Nonlinearities, nonstationarities and the (ab)use of correlation analyses hamper the discovery of true causal patterns. We here introduce a deep learning methodology that extracts nonlinear latent functions from spatio-temporal Earth data and that are Granger causal with the index altogether. We illustrate its use to study the impact of ENSO on vegetation, which allows for a more rigorous study of impacts on ecosystems globally. Authors: Gherardo Varando (Universitat de València); Miguel-Ángel Fernández-Torres (Universitat de València); Gustau Camps-Valls (Universitat de València)	Climate Science & Modeling Interpretable ML Unsupervised & Semi-Supervised Learning
ICML 2021	DeepPolicyTracker: Tracking Changes In Environmental Policy In The Brazilian Federal Official Gazette With Deep Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Even though most of its energy generation comes from renewable sources, Brazil is one of the largest emitters of greenhouse gases in the world, due to intense farming and deforestation of biomes, such as the Amazon Rainforest, whose preservation is essential for compliance with the Paris Agreement. Still, regardless of lobbies or prevailing political orientation, all government legal actions are published daily in the Federal Official Gazette. However, with hundreds of decrees issued every day by the authorities, it is absolutely burdensome to manually analyze all these processes and find out which ones can pose serious environmental hazards. In this paper, we propose the DeepPolicyTracker, a promising deep learning model that uses a state-of-the-art pre-trained natural language model to classify government acts and track harmful changes in the environmental policies. We also provide the used dataset annotated by domain experts and show some results already obtained. In the future, this system should serve to scale up the high-quality tracking of all oficial documents with a minimum of human supervision and contribute to increasing society's awareness of every government action. Authors: Flávio N Cação (University of Sao Paulo); Anna Helena Reali Costa (Universidade de São Paulo); Natalie Unterstell (Política por Inteiro); Liuca Yonaha (Política por Inteiro); Taciana Stec (Política por Inteiro); Fábio Ishisaki (Política por Inteiro)	Natural Language Processing
ICML 2021	Fast-Slow Streamflow Model Using Mass-Conserving LSTM (Papers Track) Abstract and authors: (click to expand) Abstract: Streamflow forecasting is key to effectively managing water resources and preparing for the occurrence of natural calamities being exacerbated by climate change. Here we use the concept of fast and slow flow components to create a new mass-conserving Long Short-Term Memory (LSTM) neural network model. It uses hydrometeorological time series and catchment attributes to predict daily river discharges. Preliminary results evidence improvement in skills for different scores compared to the recent literature. Authors: Miguel Paredes Quinones (IBM Research); Maciel Zortea (IBM Research); Leonardo Martins (IBM Research)	Climate Science & Modeling Time-series Analysis
ICML 2021	Attention For Damage Assessment (Papers Track) Abstract and authors: (click to expand) Abstract: Due to climate change the hurricanes are getting stronger and having longer impacts. To reduce the detrimental effects of these hurricanes faster and accurate assessments of damages are essential to the rescue teams. Like other computer vision techniques semantic segmentation can identify the damages and help in proper and prompt damage assessment. Current segmentation methods can be classified into attention and non-attention based methods. Existing non-attention based methods suffers from low accuracy and therefore attention based methods are becoming popular. Self-attention based methods can map the mutual relationship and dependencies among pixels of an image and thus improve semantic segmentation accuracy. In this paper, we present a self-attention semantic segmentation method on UAV imageries to assess the damages inflicted by a natural disaster. The proposed method outperforms four state-of-art segmentation methods both quantitatively and qualitatively with a mean IoU score of 84.03 %. Authors: Tashnim Chowdhury (University of Maryland Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)	Disaster Management and Relief Buildings Computer Vision & Remote Sensing
ICML 2021	Online LSTM Framework for Hurricane Trajectory Prediction (Papers Track) Abstract and authors: (click to expand) Abstract: Hurricanes are high-intensity tropical cyclones that can cause severe damages when the storms make landfall. Accurate long-range prediction of hurricane trajectories is an important but challenging problem due to the complex interactions between the ocean and atmosphere systems. In this paper, we present a deep learning framework for hurricane trajectory forecasting by leveraging the outputs from an ensemble of dynamical (physical) models. The proposed framework employs a temporal decay memory unit for imputing missing values in the ensemble member outputs, coupled with an LSTM architecture for dynamic path prediction. The framework is extended to an online learning setting to capture concept drift present in the data. Empirical results suggest that the proposed framework significantly outperforms various baselines including the official forecasts from U.S. National Hurricane Center (NHC). Authors: Ding Wang (Michigan State University); Pang-Ning Tan (MSU)	Disaster Management and Relief Data Mining
ICML 2021	Controlling Weather Field Synthesis Using Variational Autoencoders (Papers Track) Abstract and authors: (click to expand) Abstract: One of the consequences of climate change is an observed increase in the frequency of extreme climate events. That poses a challenge for weather forecast and generation algorithms, which learn from historical data but should embed an often uncertain bias to create correct scenarios. This paper investigates how mapping climate data to a known distribution using variational autoencoders might help explore such biases and control the synthesis of weather fields towards more extreme climate scenarios. We experimented using a monsoon-affected precipitation dataset from southwest India, which should give a roughly stable pattern of rainy days and ease our investigation. We report compelling results showing that mapping complex weather data to a known distribution implements an efficient control for weather field synthesis towards more (or less) extreme scenarios. Authors: Dario Augusto Borges Oliveira (IBM Research); Jorge Luis Guevara Diaz (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)	Climate Science & Modeling Generative Modeling
ICML 2021	ForestViT: A Vision Transformer Network for Convolution-free Multi-label Image Classification in Deforestation Analysis (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding the dynamics of deforestation as well as land uses of neighboring areas is of vital importance for the design and development of appropriate forest conservation and management policies. In this paper, we approach deforestation as a multi-label classification problem in an endeavor to capture the various relevant land uses from satellite images. To this end, we propose a multi-label vision transformer model, ForestViT, which leverages the benefits of self-attention mechanism, obviating any convolution operations involved in commonly used deep learning models utilized for deforestation detection. Authors: Maria Kaselimi (National Technical University of Athens); Athanasios Voulodimos (University of West Attica); Ioannis Daskalopoulos (University of West Attica); Nikolaos Doulamis (National Technical University of Athens); Anastasios Doulamis (Technical University of Crete)	Computer Vision & Remote Sensing Forests
ICML 2021	Reducing Carbon in the Design of Large Infrastructure Scheme with Evolutionary Algorithms (Papers Track) Abstract and authors: (click to expand) Abstract: The construction and operations of large infrastructure schemes such as railways, roads, pipelines and power lines account for a significant proportion of global carbon emissions. Opportunities to reduce the embodied and operational carbon emissions of new infrastructure schemes are greatest during the design phase. However, schedule and cost constraints limit designers from assessing a large number of design options in detail to identify the solution with the lowest lifetime carbon emissions using conventional methods. Here, we develop an evolutionary algorithm to rapidly evaluate in detail the lifetime carbon emissions of thousands of possible design options for new water transmission pipeline schemes. Our results show that this approach can help designers in some cases to identify design solutions with more than 10% lower operational carbon emissions compared with conventional methods, saving more than 1 million tonnes in lifetime carbon emissions for a new water transmission pipeline scheme. We also find that this evolutionary algorithm can be applied to design other types of infrastructure schemes such as non-water pipelines, railways, roads and power lines. Authors: Matt Blythe (Continuum Industries)	Buildings Heavy Industry and Manufacturing Power & Energy Transportation
ICML 2021	An Accurate and Scalable Subseasonal Forecasting Toolkit for the United States (Papers Track) Abstract and authors: (click to expand) Abstract: We develop a subseasonal forecasting toolkit of accurate and highly scalable benchmarks that outperform both the United States operational Climate Forecasting System (CFSv2) and state-of-the-art learning methods from the literature. Our new learned benchmarks include (a) Climatology++, an enhanced form of climatology using knowledge of only the day of the year; (b) CFSv2++, a learned correction for CFSv2; and (c) Persistence++, an augmented persistence model that combines lagged measurements with CFSv2forecasts. These methods alone improve upon CFSv2 accuracy by 9% for US precipitation and 6% for US temperature over 2011-2020. Ensembling our benchmarks with diverse forecasting methods leads to even further gains. Overall, we find that augmenting classical forecasting approaches with learned corrections yields an effective, low-cost strategy for building next-generation subseasonal forecasting models. Authors: Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Miruna Oprescu (Microsoft Research); Judah Cohen (AER); Franklyn Wang (Harvard); Sean Knight (MIT); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research)
ICML 2021	Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. Historically, monitoring water usage has been a slow and expensive manual process with many imperfections and abuses. Ma-chine learning and remote sensing developments have increased the ability to automatically monitor irrigation patterns, but existing techniques often require curated and labelled irrigation data, which are expensive and time consuming to obtain and may not exist for impactful areas such as developing countries. In this paper, we explore an end-to-end real world application of irrigation detection with uncurated and unlabeled satellite imagery. We apply state-of-the-art self-supervised deep learning techniques to optical remote sensing data, and find that we are able to detect irrigation with up to nine times better precision, 90% better recall and 40% more generalization ability than the traditional supervised learning methods. Authors: Chitra Agastya (UC Berkeley, IBM); Sirak Ghebremusse (UC Berkeley); Ian Anderson (UC Berkeley); Colorado Reed (UC Berkeley); Hossein Vahabi (University California Berkeley); Alberto Todeschini (UC Berkeley)	Unsupervised & Semi-Supervised Learning Climate Science & Modeling Computer Vision & Remote Sensing Meta- and Transfer Learning Agriculture & Food
ICML 2021	Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network (Papers Track) Abstract and authors: (click to expand) Abstract: An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at seasonal scales remains an outstanding challenge. Here, we present an approach to forecasting the quantiles of the maximum daily precipitation in each week up to six months ahead using the temporal fusion transformer (TFT) model. Through experiments in two regions, we compare TFT predictions with those of two baselines: climatology and a calibrated ECMWF SEAS5 ensemble forecast (S5). Our results show that, in terms of quantile risk at six month lead time, the TFT predictions significantly outperform those from S5 and show an overall small improvement compared to climatology. The TFT also responds positively to departures from normal that climatology cannot. Authors: Daniel Salles Civitarese (IBM Research, Brazil); Daniela Szwarcman (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)	Disaster Management and Relief Climate Science & Modeling Time-series Analysis
ICML 2021	BERT Classification of Paris Agreement Climate Action Plans (Papers Track) Abstract and authors: (click to expand) Abstract: As the volume of text-based information on climate policy increases, natural language processing (NLP) tools can distill information from text to better inform decision making on climate policy. We investigate how large pretrained transformers based on the BERT architecture classify sentences on a dataset of climate action plans which countries submitted to the United Nations following the 2015 Paris Agreement. We use the document header structure to assign noisy policy-relevant labels such as mitigation, adaptation, energy, and land use to text elements. Our models provide an improvement in out-of-sample classification over simple heuristics though fall short of the consistency observed between human annotators. We hope to extend this framework to a wider class of textual climate change data such as climate legislation and corporate social responsibility filings and build tools to streamline the extraction of information from these documents for climate change researchers. Authors: Tom Corringham (Scripps Institution of Oceanography); Daniel Spokoyny (Carnegie Mellon University); Eric Xiao (University of California San Diego); Christopher Cha (University of California San Diego); Colin Lemarchand (University of California San Diego); Mandeep Syal (University of California San Diego); Ethan Olson (University of California San Diego); Alexander Gershunov (Scripps Institution of Oceanography)	Natural Language Processing Climate Policy
ICML 2021	Quantification of Carbon Sequestration in Urban Forests (Papers Track) Abstract and authors: (click to expand) Abstract: Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere, however, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species, which are crucial attributes in carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from remote imagery in order to calculate the tree's biomass. Specifically, for Manhattan, New York City, we estimate a total of 52,000 tons of carbon sequestered in trees. Authors: Levente Klein (IBM Research); Wang Zhou (IBM Research); Conrad M Albrecht (IBM Research)	Carbon Capture & Sequestration Climate Science & Modeling Forests Land Use
ICML 2021	A comparative study of stochastic and deep generative models for multisite precipitation synthesis (Papers Track) Abstract and authors: (click to expand) Abstract: Future climate change scenarios are usually hypothesized using simulations from weather generators. However, there only a few works comparing and evaluating multisite weather generators, and there is no existing work contrasting promising deep learning approaches for weather generation against classical stochastic weather generators. This study shows preliminary results evaluating stochastic weather generators and deep generative models for multisite precipitation synthesis. Using a variety of metrics, we compare two open source weather generators: XWeathergen (an extension of the Weathergen library) and RGeneratePrec, and two deep generative models: GAN and VAE. Our preliminary results can serve as a guide for improving the design of deep learning architectures and algorithms for the multisite precipitation synthesis task. Authors: Jorge Luis Guevara Diaz (IBM Research); Dario Augusto Borges Oliveira (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)	Generative Modeling
ICML 2021	Self-Supervised Learning on Multispectral Satellite Data for Near-Term Solar Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: With the unprecedented increase in distributed photovoltaic (PV) capacity across the globe, there is an increasing need for reliable and accurate forecasting of solar power generation. While PV output is affected by many factors, the atmosphere, i.e., cloud cover, plays a dominant role in determining the amount of downwelling solar irradiance that reaches PV modules. This paper demonstrates that self-supervised learning of multispectral satellite data from the recently launched GOES-R series of satellites can improve near-term (15 minutes) solar forecasting. We develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across many solar sites on the raw spatio-temporal data from GOES-R satellites. This self-supervised model provides estimates of future solar irradiance that can be fed directly to a regression model trained on smaller site-specific solar data to provide near-term solar PV forecasts at the site. The regression implicitly models site-specific characteristics, such as capacity, panel tilt, orientation, etc, while the self-supervised CNN-LSTM implicitly captures global atmospheric patterns affecting a site's solar irradiation. Results across 25 solar sites show the utility of such self-supervised modeling by providing accurate near-term forecast with errors close to that of a model using current ground-truth observations. Authors: Akansha Singh Bansal (University of Massachusetts Amherst); Trapit Bansal (University of Massachusetts Amherst); David Irwin (University of Massachusetts Amherst)	Unsupervised & Semi-Supervised Learning Time-series Analysis
ICML 2021	Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Rise in global temperatures is resulting in polar ice caps to melt away, which can lead to drastic sea level rise and coastal floods. Accurate calculation of the ice cap reduction is necessary in order to project its climatic impact. Ice sheets are monitored through Snow Radar sensors which give noisy profiles of subsurface ice layers. The sensors take snapshots of the entire ice sheet regularly, and thus result in large datasets. In this work, we use convolutional neural networks (CNNs) for their property of feature extraction and generalizability on large datasets. We also use wavelet transforms and embed them as a layer in the architecture to help in denoising the radar images and refine ice layer detection. Our results show that incorporating wavelets in CNNs helps in detecting the position of deep subsurface ice layers, which can be used to analyse their change overtime. Authors: Debvrat Varshney (University of Maryland Baltimore County); Masoud Yari (College of Engineering and Information Technology, University of Maryland Balitimore County); Tashnim Chowdhury (University of Maryland Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)	Computer Vision & Remote Sensing Climate Science & Modeling
ICML 2021	Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM (Papers Track) Abstract and authors: (click to expand) Abstract: Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. Authors: Sahara Ali (University of Maryland, Baltimore County); Yiyi Huang (University of Maryland, Baltimore County); Xin Huang (University of Maryland, Baltimore County); Jianwu Wang (University of Maryland, Baltimore County)	Climate Science & Modeling Ecosystems & Biodiversity Time-series Analysis
ICML 2021	Toward efficient calibration of higher-resolution Earth System Models (Papers Track) Best Paper: Pathway to Impact Abstract and authors: (click to expand) Abstract: Projections of future climate change to support decision-making require high spatial resolution, but this is computationally prohibitive with modern Earth system models (ESMs). A major challenge is the calibration (parameter tuning) process, which requires running large numbers of simulations to identify the optimal parameter values. Here we train a convolutional neural network (CNN) on simulations from two lower-resolution (and thus much less expensive) versions of the same ESM, and a smaller number of higher-resolution simulations. Cross-validated results show that the CNN's skill exceeds that of a climatological baseline for most variables with as few as 5-10 examples of the higher-resolution ESM, and for all variables (including precipitation) with at least 20 examples. This proof-of-concept study offers the prospect of significantly more efficient calibration of ESMs, by reducing the required CPU time for calibration by 20-40 %. Authors: Christopher Fletcher (University of Waterloo); William McNally (University of Waterloo); John Virgin (University of Waterloo)	Generative Modeling Climate Science & Modeling
ICML 2021	Visual Question Answering: A Deep Interactive Framework for Post-Disaster Management and Damage Assessment (Papers Track) Abstract and authors: (click to expand) Abstract: Each natural disaster has left a trail of destruction and damage, which must be managed very effectively to reduce the disaster's impact. Lack of proper decision making in post-disaster managerial level can increase human suffering and waste a great amount of money. Our objective is to incorporate a deep interactive approach in the decision-making system especially in a rescue mission after any natural disaster for the systematic distribution of the limited resources and accelerating the recovery process. We believe that Visual Question Answering (VQA) is the finest way to address this issue. In visual question answering, a query-based answer regarding the situation of the affected area can add value to the decision-making system. Our main purpose of this study is to develop a Visual Question Answering model for post-disaster damage assessment purposes. With this aim, we collect the images by UAV (Unmanned Aerial Vehicle) after Hurricane Harvey and develop a dataset that includes the questions that are very important in the decision support system after a natural disaster. In addition, We propose a supervised attention-based approach in the modeling segment. We compare our approach with the two other baseline attention-based VQA algorithms namely Multimodal Factorized Bilinear (MFB) and Stacked Attention Network (SAN). Our approach outperforms in providing answers for several types of queries including simple counting, complex counting compares to the baseline models. Authors: Argho Sarkar (University of Maryland, Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)	Disaster Management and Relief Computer Vision & Remote Sensing
ICML 2021	Designing Bounded min-knapsack Bandits algorithm for Sustainable Demand Response (Papers Track) Abstract and authors: (click to expand) Abstract: Around 40% of global energy produced is consumed by buildings. By using renewable energy resources we can alleviate the dependence on electrical grids. Recent trends focus on incentivizing consumers to reduce their demand consumption during peak hours for sustainable demand response. To minimize the loss, the distributor companies should target the right set of consumers and demand the right amount of electricity reductions. This paper proposes a novel bounded integer min-knapsack algorithm and shows that the algorithm, while allowing for multiple unit reduction, also optimizes the loss to the distributor company within a factor of two (multiplicative) and a problem-dependent additive constant. Existing CMAB algorithms fail to work in this setting due to non-monotonicity of reward function and time-varying optimal sets. We propose a novel algorithm Twin-MinKPDR-CB to learn these compliance probabilities efficiently. Twin-MinKPDR-CB works for non-monotone reward functions bounded min-knapsack constraints and time-varying optimal sets. We find that Twin-MinKPDR-CB achieves sub-linear regret of O(log T) with T being the number of rounds demand response is run. Authors: Akansha Singh (Indian Institute of Technology, Ropar); Meghana Reddy (Indian Institute of Technology, Ropar); Zoltan Nagy (University of Texas); Sujit P. Gujar (Machine Learning Laboratory, International Institute of Information Technology, Hyderabad); Shweta Jain (Indian Institute of Technology Ropar)	Power & Energy Buildings Reinforcement Learning Uncertainty Quantification & Robustness
ICML 2021	Sky Image Prediction Using Generative Adversarial Networks for Solar Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Large-scale integration of solar photovoltaics (PV) is challenged by high variability in its power output, mainly due to local and short-term cloud events. To achieve accurate solar forecasting, it is paramount to accurately predict the movement of clouds. Here, we use generative adversarial networks (GANs) to predict future sky images based on past sky image sequences and show that our trained model can generate realistic future sky images and capture the dynamics of clouds in the context frames. The generated images are then evaluated for a downstream solar forecasting task; results show promising performance. Authors: Yuhao Nie (Stanford University); Andea Scott (Stanford University); Eric Zelikman (Stanford University); Adam Brandt (Stanford University)	Generative Modeling Climate Science & Modeling Computer Vision & Remote Sensing
ICML 2021	EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations (Papers Track) Abstract and authors: (click to expand) Abstract: The nexus between transportation, the power grid, and consumer behavior is much more pronounced than ever before as the race to decarbonize intensifies. Electrification in the transportation sector has led to technology shifts and rapid deployment of electric vehicles (EVs). The potential increase in stochastic and spatially heterogeneous charging load presents a unique challenge that is not well studied, and will have significant impacts on grid operations, emissions, and system reliability if not managed effectively. Realistic scenario generators can help operators prepare, and machine learning can be leveraged to this end. In this work, we develop generative adversarial networks (GANs) to learn distributions of electric vehicle (EV) charging sessions and disentangled representations. We show that this model successfully parameterizes unlabeled temporal and power patterns and is able to generate synthetic data conditioned on these patterns. We benchmark the generation capability of this model with Gaussian Mixture Models (GMMs), and empirically show that our proposed model framework is better at capturing charging distributions and temporal dynamics. Authors: Robert Buechler (Stanford University); Emmanuel O Balogun (Stanford University); Arun Majumdar (Stanford University); Ram Rajagopal (Stanford University)	Power & Energy Transportation Generative Modeling Unsupervised & Semi-Supervised Learning
ICML 2021	Reconstruction of Long-Term Historical Electricity Demand Data (Papers Track) Abstract and authors: (click to expand) Abstract: Long-term planning of a robust power system requires the understanding of changing demand patterns. Electricity demand is highly weather sensitive. Thus, the supply side variation from introducing intermittent renewable sources, juxtaposed with variable demand, will introduce additional challenges in the grid planning process. By understanding the spatial and temporal variability of temperature over the US, the response of demand to natural variability and climate change-related effects on temperature can be separated, especially because the effects due to the former factor are not known. Through this project, we aim to better support the technology& policy development process for power systems by developing machine and deep learning ’back-forecasting’ models to reconstruct multidecadaldemand records and study the natural variabilityof temperature and its influence on demand. Authors: Reshmi Ghosh (Carnegie Mellon University); Michael Craig (University of Michigan); H.Scott Matthews (Carnegie Mellon University); Laure Berti-Equille (IRD)
ICML 2021	A Set-Theoretic Approach to Safe Reinforcement Learning in Power Systems (Papers Track) Abstract and authors: (click to expand) Abstract: Reducing the carbon footprint of the energy sector will be a vital part of the fight against climate change, and doing so will require the widespread adoption of renewable energy resources. Optimally integrating a large number of these resources requires new control techniques that can both compensate for the variability of renewables and satisfy hard engineering constraints. Reinforcement learning (RL) is a promising approach to data-driven control, but it is difficult to verify that the policies derived from data will be safe. In this paper, we combine RL with set-theoretic control to propose a computationally efficient approach to safe RL. We demonstrate the method on a simplified power system model and compare it with other RL techniques. Authors: Daniel Tabas (University of Washington); Baosen Zhang (University of Washington)	Reinforcement Learning Power & Energy
ICML 2021	A study of battery SoC scheduling using machine learning with renewable sources (Papers Track) Abstract and authors: (click to expand) Abstract: An open energy system (OES) enables the shared distribution of energy resources within a community autonomously and efficiently. For this distributed system a rooftop solar panel and a battery are installed in each house of the community. The OES system monitors the State of Charge (SoC) of each battery independently, arbitrates energy-exchange requests from each house, and physically controls peer-to-peer energy exchanges. In this study, our goal is to optimize those energy exchanges to maximize the renewable energy penetration within the community using machine learning techniques. Future household electricity consumption is predicted using machine learning from the past time series. The predicted consumption is used to determine the next energy-exchange strategy, i.e. when and how much energy should be exchanged to minimize the surplus of solar energy. The simulation results show that the proposed method can increase the amount of renewable energy penetration within the community. Authors: Daisuke Kawamoto (Sony Computer Science Laboratories, Inc.); Gopinath Rajendiran (CSIR Central Scientific Instruments Organisation, Chennai)	Power & Energy Time-series Analysis
ICML 2021	Multivariate climate downscaling with latent neural processes (Papers Track) Abstract and authors: (click to expand) Abstract: Statistical downscaling is a vital tool in generating high resolution projections for climate impact studies. This study applies convolutional latent neural processes to multivariate downscaling of maximum temperature and precipitation. In contrast to existing downscaling methods, this model is shown to produce spatially coherent predictions at arbitrary locations specified at test time, regardless of whether training data are available at these points. Authors: Anna Vaughan (Univeristy of Cambridge); Nic Lane (University of Cambridge); Michael Herzog (University of Cambridge)	Climate Science & Modeling Causal & Bayesian Methods
ICML 2021	FIRE-ML: A Remotely-sensed Daily Wildfire Forecasting Dataset for the Contiguous United States (Papers Track) Abstract and authors: (click to expand) Abstract: Wildfires are natural phenomena that can have devastating effects on ecosystems, urban developments, and the environment. Improving the scientific understanding of these events and the ability to forecast how they will evolve in the short- and long-term are ongoing multi-decadal challenges. We present a large-scale dataset, well-suited to machine learning, that aggregates and aligns multiple remotely-sensed and forecasted data products to provide a holistic set of features for forecasting wildfires on daily timescales. This dataset includes 4.2 million unique active fire detections, covers the majority of the contiguous United States from 2012 to 2020, and includes active fire detections, land cover, topography, and meteorology. Authors: Casey A Graff (UC Irvine)	Computer Vision & Remote Sensing Land Use
ICML 2021	IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation (Papers Track) Abstract and authors: (click to expand) Abstract: Effective environmental planning and management to address climate change could be achieved through extensive environmental modeling with machine learning and conventional physical models. In order to develop and improve these models, practitioners and researchers need comprehensive benchmark datasets that are prepared and processed with environmental expertise that they can rely on. This study presents an extensive dataset of rainfall events for the state of Iowa (2016-2019) acquired from the National Weather Service Next Generation Weather Radar (NEXRAD) system and processed by a quantitative precipitation estimation system. The dataset presented in this study could be used for better disaster monitoring, response and recovery by paving the way for both predictive and prescriptive modeling. Authors: Muhammed A Sit (The University of Iowa); Bongchul Seo (IIHR—Hydroscience & Engineering, The University of Iowa); Ibrahim Demir (The University of Iowa)	Data Mining
ICML 2021	Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks (Papers Track) Abstract and authors: (click to expand) Abstract: The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction. Authors: Muhammed A Sit (The University of Iowa); Bekir Demiray (The University of Iowa); Ibrahim Demir (The University of Iowa)	Disaster Management and Relief Climate Science & Modeling Computer Vision & Remote Sensing Interpretable ML Time-series Analysis
ICML 2021	Wildfire Smoke Plume Segmentation Using Geostationary Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Wildfires have increased in frequency and severity over the past two decades, especially in the Western United States. Beyond physical infrastructure damage caused by these wildfire events, researchers have increasingly identified harmful impacts of particulate matter generated by wildfire smoke on respiratory, cardiovascular, and cognitive health. This inference is difficult due to the spatial and temporal uncertainty regarding how much particulate matter is specifically attributable to wildfire smoke. One factor contributing to this challenge is the reliance on manually drawn smoke plume annotations, which are often noisy representations limited to the United States. This work uses deep convolutional neural networks to segment smoke plumes from geostationary satellite imagery. We compare the performance of predicted plume segmentations versus the noisy annotations using causal inference methods to estimate the amount of variation each explains in Environmental Protection Agency (EPA) measured surface level particulate matter <2.5μm in diameter (PM2.5). Authors: Jeffrey L Wen (Stanford University); Marshall Burke (Stanford University)	Computer Vision & Remote Sensing Behavioral and Social Science Climate Science & Modeling Disaster Management and Relief Societal Adaptation & Resilience
ICML 2021	Deep Spatial Temporal Forecasting of Electrical Vehicle Charging Demand (Papers Track) Abstract and authors: (click to expand) Abstract: Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. Short and long-term charging demand forecasting will allow for better optimisation of the power grid and future infrastructure expansions. In this paper, we propose to use publicly available data to forecast the electric vehicle charging demand. To model the complex spatial-temporal correlations between charging stations, we argue that Temporal Graph Convolution Models are the most suitable to capture the correlations. The proposed Temporal Graph Convolutional Networks provide the most accurate forecasts for short and long-term forecasting compared with other forecasting methods. Authors: Frederik B Hüttel (Technical University of Denmark (DTU)); Filipe Rodrigues (Technical University of Denmark (DTU)); Inon Peled (Technical University of Denmark (DTU)); Francisco Pereira (DTU)	Transportation Power & Energy Time-series Analysis
ICML 2021	Powering Effective Climate Communication with a Climate Knowledge Base (Proposals Track) Abstract and authors: (click to expand) Abstract: While many accept climate change and its growing impacts, few converse about it well, limiting the adoption speed of societal changes necessary to address it. In order to make effective climate communication easier, we aim to build a system that presents to any individual the climate information predicted to best motivate and inspire them to take action given their unique set of personal values. To alleviate the cold-start problem, the system relies on a knowledge base (ClimateKB) of causes and effects of climate change, and their associations to personal values. Since no such comprehensive ClimateKB exists, we revisit knowledge base construction techniques and build a ClimateKB from free text. We plan to open source the ClimateKB and associated code to encourage future research and applications. Authors: Kameron B. Rodrigues (Stanford University); Shweta Khushu (SkySpecs Inc); Mukut Mukherjee (ClimateMind); Andrew Banister (Climate Mind); Anthony Hevia (ClimateMind); Sampath Duddu (ClimateMind); Nikita Bhutani (Megagon Labs)	Natural Language Processing
ICML 2021	Solar PV Maps for Estimation and Forecasting of Distributed Solar Generation (Proposals Track) Abstract and authors: (click to expand) Abstract: Rapid uptake of distributed solar PV is starting to make the operation of grids and energy markets more challenging, and better methods are needed for measuring and forecasting distributed solar PV generation across entire regions. We propose a method for converting time series data from a number of point sources (power measurements at individual sites) into 2-dimensional maps that estimate total solar PV generation across large areas. These maps may be used on their own, or in conjunction with additional data sources (such as satellite imagery) in a deep learning framework that enables improved regional solar PV estimation and forecasting. We provide some early validation and results, discuss anticipated benefits of this approach, and argue that this method has the potential to further enable significant uptake of solar PV, assisting a shift away from fossil fuel-based generation. Authors: Julian de Hoog (The University of Melbourne); Maneesha Perera (The University of Melbourne); Kasun Bandara (The University of Melbourne); Damith Senanayake (The University of Melbourne); Saman Halgamuge (University of Melbourne)	Power & Energy Time-series Analysis
ICML 2021	An Iterative Approach to Finding Global Solutions of AC Optimal Power Flow Problems (Proposals Track) Abstract and authors: (click to expand) Abstract: To achieve a cleaner energy system, a diverse set of energy resources such as solar PV, battery storage and electric vehicles are entering the electric grid. Their operation is typically controlled by solving a resource allocation problem, called the AC optimal power flow (ACOPF) problem. This problem minimizes the cost of generation subject to supply/demand balance and various other engineering constraints. It is nonlinear and nonconvex, and existing solvers are generally successful in finding local solutions. As the share of renewable energy resources increases, it is becoming increasingly important to find globally optimal solutions to utilize these resources to the full extent. In this paper, we propose a simple iterative approach to find globally optimal solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem and we form a partial Lagrangian from the associated dual variables. This partial Lagrangian has a much better optimization landscape and we use its solution as a warm start for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. We demonstrate the effectiveness of our algorithm on standard benchmarks. We also show how the dual variables could be found by using a neural network to further speed up the algorithm. Authors: Ling Zhang (University of Washington); Baosen Zhang (University of Washington)	Power & Energy
ICML 2021	Deep learning applied to sea surface semantic segmentation: Filtering sunglint from aerial imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: Water waves are an ubiquitous feature of the oceans, which serve as a pathway for interactions with the atmosphere. Wave breaking in particular is crucial in developing better understanding of the exchange of momentum, heat, and gas fluxes between the ocean and the atmosphere. Characterizing the properties of wave breaking using orbital or suborbital imagery of the surface of the ocean can be challenging, due to contamination from sunglint, a persistent feature in certain lighting conditions. Here we propose a supervised learning approach to accurately detect whitecaps from airborne imagery obtained under a broad range of lighting conditions. Finally, we discuss potential applications for improving ocean and climate models. Authors: Teodor Vrecica (UCSD); Quentin Paletta (University of Cambridge); Luc Lenain (UCSD)	Climate Science & Modeling Computer Vision & Remote Sensing
ICML 2021	Technical support project and analysis of the dissemination of carbon dioxide and methane from Lake Kivu in nature and its impact on biodiversity in the Great Lakes region since 2012 (Proposals Track) Abstract and authors: (click to expand) Abstract: Straddling the Democratic Republic of the Congo and Rwanda, at an altitude of 1,460 m, Lake Kivu is one of the ten great lakes in Africa, alongside the main ones that are Victoria and Tanganyika. Kivu contains very high concentrations of gases (carbon dioxide and methane in particular), produced by volcanic activity in the region and the decomposition of organic matter. It has 2,700 km2 of this body of water, a depth that approaches 500 meters in places. It is estimated to contain 60 billion cubic meters of dissolved methane and about 300 billion cubic meters of carbon dioxide accumulated over time. Lake Kivu, located north of Lake Tanganyika and contains a very high amount of carbon dioxide and methane. Carbon dioxide (CO2) and methane (CH4) are both greenhouse gases that affect how well the planet works. The first stays in the atmosphere for a hundred years while the second stays there only for a dozen years. The effect of the dissemination of these in nature prompts me to collect as much data as possible on their circulation and to suggest possible solutions that are consistent with the Paris Agreement. In addition, many wastes come from households and/or small industries in the towns of Bukavu, Goma for the DRC and those of Gyangugu and Gisenyi for Rwanda constitute a high source of CH4 emissions which also contribute to global warming. The exploitation of methane expected in the near future is an additional threat to the sustainable development of ecosystem resources. For various reasons, Lake Kivu constitutes an adequate model for studying the responses of large tropical lakes to changes linked to human activity: indeed, despite its physical and biogeochemical peculiarities, the limnological and ecological processes of its pelagic waters are subject to the same forcings as in other large lakes in the same region, as shown by recent studies. Authors: Bulonze Chibaderhe (FEMAC Asbl)	Reinforcement Learning Carbon Capture & Sequestration
ICML 2021	Virtual Screening for Perovskites Discovery (Proposals Track) Abstract and authors: (click to expand) Abstract: Re-inventing the global energy harvesting system from fossil fuels to renewables is essential for reducing greenhouse gas emissions in line with current climate targets. Perovskite photovoltaics (PVs), the class of materials with relatively unexplored material configurations, play key role in solar energy generation due to their low manufacturing cost and exceptional optoelectronic properties. Without the efficient utilisation of machine learning, the discovery and manufacturing process could take a dangerously long time. We present a high-throughput computational design that leverages machine learning algorithms at various steps in order to assess the suitability of the organic molecules for the perovskite crystals. Authors: Andrea Karlova (UCL); Cameron C.L. Underwood (University of Surrey); Ravi Silva (University of Surrey)	Power & Energy
ICML 2021	Leveraging Machine Learning for Equitable Transition of Energy Systems (Proposals Track) Abstract and authors: (click to expand) Abstract: Our planet is facing overlapping crises of climate change, global pandemic, and systemic inequality. To respond climate change, the energy system is in the midst of its most foundational transition since its inception, from traditional fuel-based energy sources to clean renewable sources. While the transition to a low-carbon energy system is in progress, there is an opportunity to make the new system more just and equitable than the current one that is inequitable in many forms. Measuring inequity in the energy system is a formidable task since it is large scale and the data is coming from abundant data sources. In this work, we lay out a plan to leverage and develop scalable machine learning (ML) tools to measure the equity of the current energy system and to facilitate a just transition to a clean energy system. We focus on two concrete examples. First, we explore how ML can help to measure the inequity in the energy inefficiency of residential houses in the scale of a town or a country. Second, we explore how deep learning techniques can help to estimate the solar potential of residential buildings to facilitate a just installation and incentive allocation of solar panels. The application of ML for energy equity is much broader than the above two examples and we highlight some others as well. The result of this research could be used by policymakers to efficiently allocate energy assistance subsidies in the current energy systems and to ensure justice in their energy transition plans. Authors: Enea Dodi (UMass Amherst); Anupama A Sitaraman (University of Massachusetts Amherst); Mohammad Hajiesmaili (UMass Amherst); Prashant Shenoy (University of Massachusetts Amherst)	Climate Justice Time-series Analysis
ICML 2021	Long-term Burned Area Reconstruction through Deep Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Wildfire impact studies are significantly hampered by the absence of a global long-term burned area dataset. This prevents conclusive statements on the role of anthropogenic activity on wildfire impacts over the last century. Here, we propose a workflow to construct a 1901-2014 reanalysis of monthly global burned area at a 0.5° by 0.5° scale. A neural network will be trained with weather-related, vegetational, societal and economic input parameters, and burned area as output label for the 1982-2014 time period. This model can then be applied to the whole 1901-2014 time period to create a data-driven, long-term burned area reanalysis. This reconstruction will allow to investigate the long-term effect of anthropogenic activity on wildfire impacts, will be used as basis for detection and attribution studies and could help to reduce the uncertainties in future predictions. Authors: Seppe Lampe (Vrije Universiteit Brussel); Bertrand Le Saux (European Space Agency (ESA)); Inne Vanderkelen (Vrije Universiteit Brussel); Wim Thiery (Vrije Universiteit Brussel)	Disaster Management and Relief Computer Vision & Remote Sensing
ICML 2021	Preserving the integrity of the Canadian northern ecosystems through insights provided by reinforcement learning-based Arctic fox movement models (Proposals Track) Abstract and authors: (click to expand) Abstract: Realistic modeling of the movement of the Arctic fox, one of the main predators of the circumpolar world, is crucial to understand the processes governing the distribution of the Canadian Arctic biodiversity. Current methods, however, are unable to adequately account for complex behaviors as well as intra- and interspecific relationships. We propose to harness the potential of reinforcement learning to develop innovative models that will address these shortcomings and provide the backbone to predict how vertebrate communities may be affected by environmental changes in the Arctic, an essential step towards the elaboration of rational conservation actions. Authors: Catherine Villeneuve (Université Laval); Frédéric Dulude-De Broin (Université Laval); Pierre Legagneux (Université Laval); Dominique Berteaux (Université du Québec à Rimouski); Audrey Durand (Université Laval)	Ecosystems & Biodiversity Reinforcement Learning
ICML 2021	Street2Sat: A Machine Learning Pipeline for Generating Ground-truth Geo-referenced Labeled Datasets from Street-Level Images (Proposals Track) Abstract and authors: (click to expand) Abstract: Ground-truth labels on crop type and other variables are critically needed to develop machine learning methods that use satellite observations to combat climate change and food insecurity. These labels difficult and costly to obtain over large areas, particularly in Sub-Saharan Africa where they are most scarce. We propose Street2Sat, a new framework for obtaining large data sets of geo-referenced crop type labels obtained from vehicle-mounted cameras that can be extended to other applications. Using preliminary data from Kenya, we present promising results from this approach and identify future to improve the method before operational use in 5 countries. Authors: Madhava Paliyam (University of Maryland); Catherine L Nakalembe (University of Maryland); Kevin Liu (University of Maryland); Richard Nyiawung (University of Guelph); Hannah R Kerner (University of Maryland)	Computer Vision & Remote Sensing Agriculture & Food
ICML 2021	From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling (Proposals Track) Abstract and authors: (click to expand) Abstract: Decades of research on climate have provided a consensus that human activity has changed the climate and we are currently heading into a climate crisis. While public discussion and research efforts on climate change mitigation have increased, potential solutions need to not only be discussed but also effectively deployed. For preventing mismanagement and holding policy makers accountable, transparency and degree of information about government processes have been shown to be crucial. However, currently the quantity of information about climate change discussions and the range of sources make it increasingly difficult for the public and civil society to maintain an overview to hold politicians accountable. In response, we propose a multi-source topic aggregation system (MuSTAS) which processes policy makers speech and rhetoric from several publicly available sources into an easily digestible topic summary. MuSTAS uses novel multi-source hybrid latent Dirichlet allocation to model topics from a variety of documents. This topic digest will serve the general public and civil society in assessing where, how, and when politicians talk about climate and climate policies, enabling them to hold politicians accountable for their actions to mitigate climate change and lack thereof. Authors: Vili Hätönen (Emblica); Fiona Melzer (University of Edinburgh)	Natural Language Processing Behavioral and Social Science Climate Policy Unsupervised & Semi-Supervised Learning
ICML 2021	NeuralNERE: Neural Named Entity Relationship Extraction for End-to-End Climate Change Knowledge Graph Construction (Proposals Track) Abstract and authors: (click to expand) Abstract: This paper proposes an end-to-end Neural Named Entity Relationship Extraction model (called NeuralNERE) for climate change knowledge graph (KG) construction, directly from the raw text of relevant news articles. The proposed model will not only remove the need for any kind of human supervision for building knowledge bases for climate change KG construction (used in the case of supervised or dictionary-based KG construction methods), but will also prove to be highly valuable for analyzing climate change by summarising relationships between different factors responsible for climate change, extracting useful insights & reasoning on pivotal events, and helping industry leaders in making more informed future decisions. Additionally, we also introduce the Science Daily Climate Change dataset (called SciDCC) that contains over 11k climate change news articles scraped from the Science Daily website, which could be used for extracting prior knowledge for constructing climate change KGs. Authors: Prakamya Mishra (Independent Researcher); Rohan Mittal (Independent Researcher)	Natural Language Processing Behavioral and Social Science Climate Policy Unsupervised & Semi-Supervised Learning
ICML 2021	A multi-task learning approach to enhance sustainable biomolecule production in engineered microorganisms (Proposals Track) Abstract and authors: (click to expand) Abstract: A sustainable alternative to sourcing many materials humans need is metabolic engineering: a field that aims to engineer microorganisms into biological factories that convert renewable feedstocks into valuable biomolecules (i.e., jet fuel, medicine). Microorganism factories must be genetically optimized using predictable DNA sequence tools, however, for many organisms, the exact DNA sequence signals defining their genetic control systems are poorly understood. To better decipher these DNA signals, we propose a multi-task learning approach that uses deep learning and feature attribution methods to identify DNA sequence signals that control gene expression in the methanotroph M. buryatense. This bacterium consumes methane, a potent greenhouse gas. If successful, this work would enhance our ability to build gene expression tools to more effectively engineer M. buryatense into an efficient biomolecule factory that can divert methane pollution into valuable, everyday materials. Authors: Erin Wilson (University of Washington); Mary Lidstrom (University of Washington); David Beck (University of Washington)	Chemistry & Materials
ICML 2021	MethaNet - an AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: Methane (CH4) is one of the most powerful anthropogenic greenhouse gases with a significant impact on global warming trajectory and tropospheric air quality. Quantifying an emission rate of observed CH4 plumes from aerial or satellite images is a critical step for understanding the local distributions and subsequently prioritizing mitigation target sites. However, there exists no method that can reliably predict emission rates from detected plumes in real-time without ancillary data. Here, we trained a convolutional neural network model, called MethaNet, to predict methane point-source emission directly from high-resolution 2-D plume images without relying on other local measurements such as background wind speeds. Our results support the basis for the applicability of using deep learning techniques to quantify CH4 point sources in an automated manner over large geographical areas. MethaNet opens the way for real-time monitoring systems, not only for present and future airborne field campaigns but also for upcoming space-based observations in this decade. Authors: Siraput Jongaramrungruang (Caltech)	Computer Vision & Remote Sensing Carbon Capture & Sequestration Climate Science & Modeling
ICML 2021	Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is stored in forests is, however, still largely done via expensive, time-consuming, and sometimes unaccountable field measurements. To overcome these limitations, many verification bodies are leveraging machine learning (ML) algorithms to estimate forest carbon from satellite or aerial imagery. Aerial imagery allows for tree species or family classification, which improves on the satellite imagery-based forest type classification. However, aerial imagery is significantly more expensive to collect and it is unclear by how much the higher resolution improves the forest carbon estimation. This proposal paper describes the first systematic comparison of forest carbon estimation from aerial imagery, satellite imagery, and “ground-truth“ field measurements via deep learning-based algorithms for a tropical reforestation project. Our initial results show that forest carbon estimates from satellite imagery can overestimate above-ground biomass by more than 10-times for tropical reforestation projects. The significant difference between aerial and satellite-derived forest carbon measurements shows the potential for aerial imagery-based ML algorithms and raises the importance to extend this study to a global benchmark between options for carbon measurements. Authors: Gyri Reiersen (TUM); David Dao (ETH Zurich); Björn Lütjens (MIT); Konstantin Klemmer (University of Warwick); Xiaoxiang Zhu (Technical University of Munich,Germany); Ce Zhang (ETH)	Carbon Capture & Sequestration Computer Vision & Remote Sensing
ICML 2021	Learning Why: Data-Driven Causal Evaluations of Climate Models (Proposals Track) Abstract and authors: (click to expand) Abstract: We plan to use nascent data-driven causal discovery methods to find and compare causal relationships in observed data and climate model output. We will look at ten different features in the Arctic climate collected from public databases and from the Energy Exascale Earth System Model (E3SM). In identifying and comparing the resulting causal networks, we hope to find important differences between observed causal relationships and those in climate models. With these, climate modeling experts will be able to improve the coupling and parameterization of E3SM and other climate models. Authors: Jeffrey J Nichol (University of New Mexico); Matthew Peterson (Sandia National Laboratories); George M Fricke (UNM); Kara Peterson (Sandia National Laboratories)	Climate Science & Modeling Causal & Bayesian Methods
ICML 2021	Enhancing Laboratory-scale Flow Imaging of Fractured Geological Media with Deep Learning Super Resolution (Proposals Track) Abstract and authors: (click to expand) Abstract: Injection into deep geological formations is a promising approach for the utilization, sequestration, and removal from the atmosphere of CO2 emissions. Laboratory experiments are essential to characterize how CO2 flows and reacts in various types of geological media. We reproduce such dynamic injection processes while imaging using Computed Tomography (CT) at sufficient temporal resolution to visualize changes in the flow field. The resolution of CT, however, is on the order of 100's of micrometers and insufficient to characterize fine-scale reaction-induced alterations to micro-fractures. Super resolution deep learning is, therefore, an essential tool to improve spatial resolution of dynamic CT images. We acquired and processed pairs of multi-scale low- and high-resolution CT rock images. We also show the performance of our baseline model on fractured rock images using peak signal to noise ratio and structural similarity index. Coupling dynamic CT imaging with deep learning results in visualization with enhanced spatial resolution of about a factor of 4 thereby enabling improved interpretation. Authors: Manju Pharkavi Murugesu (Stanford University); Timothy Anderson (Stanford University); Niccolo Dal Santo (MathWorks, Inc.); Vignesh Krishnan (The MathWorks Ltd); Anthony Kovscek (Stanford University)	Carbon Capture & Sequestration Climate Science & Modeling Computer Vision & Remote Sensing Generative Modeling Unsupervised & Semi-Supervised Learning
ICML 2021	Forecasting emissions through Kaya identity using Neural ODEs (Proposals Track) Abstract and authors: (click to expand) Abstract: Starting from Kaya identity, we used a Neural ODE model to predict the evolution of several indicators related to carbon emissions, on a country-level : population, GDP per capita, energy intensity of GDP, carbon intensity of energy. We compared the model with a baseline statistical model - VAR - and obtained good performances. We conclude that this machine-learning approach can be used to produce a wide range of results and give relevant insight to policymakers. Authors: Pierre Browne (Imperial College London)	Time-series Analysis Climate Policy Power & Energy
ICML 2021	On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data (Proposals Track) Best Paper: Proposals Abstract and authors: (click to expand) Abstract: This paper discusses opportunities for developments in spatial clustering methods to help leverage broad scale community science data for building species distribution models (SDMs). SDMs are tools that inform the science and policy needed to mitigate the impacts of climate change on biodiversity. Community science data span spatial and temporal scales unachievable by expert surveys alone, but they lack the structure imposed in smaller scale studies to allow adjustments for observational biases. Spatial clustering approaches can construct the necessary structure after surveys have occurred, but more work is needed to ensure that they are effective for this purpose. In this proposal, we describe the role of spatial clustering for realizing the potential of large biodiversity datasets, how existing methods approach this problem, and ideas for future work. Authors: Mark Roth (Oregon State University); Tyler Hallman (Swiss Ornithological Institute); W. Douglas Robinson (Oregon State University); Rebecca Hutchinson (Oregon State University)	Ecosystems & Biodiversity Unsupervised & Semi-Supervised Learning
ICML 2021	Machine Learning for Climate Change: Guiding Discovery of Sorbent Materials for Direct Air Capture of CO2 (Proposals Track) Abstract and authors: (click to expand) Abstract: The global climate crisis requires interdisciplinary collaboration. The same is true for making significant strides in materials discovery for direct air capture (DAC) of carbon dioxide (CO2). DAC is an emerging technology that captures CO2 directly from the atmosphere and it is part of the solution to achieving carbon neutrality by 2050. The proposed project is a collaborative effort that tackles climate change by using machine learning to guide scientists to novel, optimized, advanced sorbent materials for direct air capture of CO2. Immediate impacts will include high throughput machine learning tools for developing new, cost-effective CO2 sorption materials, and continued, expanded collaborations with potential domestic and international stakeholders. Authors: Diana L Ortiz-Montalvo (NIST); Aaron Gilad Kusne (NIST); Austin McDannald (NIST); Daniel Siderius (NIST); Kamal Choudhary (NIST); Taner Yildirim (NIST)	Carbon Capture & Sequestration Active Learning
ICML 2021	Reducing greenhouse gas emissions by optimizing room temperature set-points (Proposals Track) Abstract and authors: (click to expand) Abstract: We design a learning and optimization framework to mitigate greenhouse gas emissions associated with heating and cooling buildings. The framework optimizes room temperature set-points based on forecasts of weather, occupancy, and the greenhouse gas intensity of electricity. We compare two approaches: the first one combines a linear load forecasting model with convex optimization that offers a globally optimal solution, whereas the second one combines a nonlinear load forecasting model with nonconvex optimization that offers a locally optimal solution. The project explores the two approaches with a simulation testbed in EnergyPlus and experiments in university-campus buildings. Authors: Yuan Cai (MIT); Subhro Das (MIT-IBM Watson AI Lab, IBM Research); Leslie Norford (Massachusetts Institute of Technology); Jeremy Gregory (Massachusetts Institute of Technology); Julia Wang (Massachusetts Institute of Technology); Kevin J Kircher (MIT); Jasmina Burek (Massachusetts Institute of Technology)	Buildings Power & Energy Time-series Analysis
ICML 2021	Deep learning network to project future Arctic ocean waves (Proposals Track) Abstract and authors: (click to expand) Abstract: The Arctic Ocean is warming at an alarming rate and will likely become ice-free in summer by mid-century. This will be accompanied by higher ocean surface waves, which pose a risk to coastal communities and marine operations. In order to develop climate change adaptation strategies, it is imperative to robustly assess the future changes in the Arctic ocean wave climate. This requires a large ensemble of regional ocean wave projections to properly capture the range of climate modeling uncertainty in the Arctic region. This has been proven challenging, as ocean wave information is typically not provided by climate models, ocean wave numerical modeling is computationally expensive, and most global wave climate ensembles exclude the Arctic region. Here we present a framework to develop a deep learning network based on CNN and LSTM which could be potentially used to obtain such a large ensemble of Arctic wave projections with an affordable cost. Authors: Merce Casas Prat (Environment and Climate Change Canada); Lluis Castrejon (Mila, Université de Montréal, Facebook AI Research); Shady Moahmmed (University of Ottawa)	Climate Science & Modeling Computer Vision & Remote Sensing
ICML 2021	Deep Learning for Spatiotemporal Anomaly Forecasting: A Case Study of Marine Heatwaves (Proposals Track) Abstract and authors: (click to expand) Abstract: Spatiotemporal data have unique properties and require specific considerations. Forecasting spatiotemporal processes is a difficult task because the data are high-dimensional, often are limited in extent, and temporally correlated. Hence, we propose to evaluate several deep learning-based approaches that are relevant to spatiotemporal anomaly forecasting. We will use marine heatwaves as a case study. Those are observed around the world and have strong impacts on marine ecosystems. The evaluated deep learning methods will be integrated for the task of marine heatwave prediction in order to overcome the limitations of spatiotemporal data and improve data-driven seasonal marine heatwave forecasts. Authors: Ding Ning (University of Canterbury); Varvara Vetrova (University of Canterbury); Karin Bryan (University of Waikato); Sébastien Delaux (Meteorological Service of New Zealand)	Climate Science & Modeling
ICML 2021	Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Machine learning in remote sensing has matured alongside a proliferation in availability and resolution of geospatial imagery, but its utility is bottlenecked by the need for labeled data. What's more, many labeled geospatial datasets are specific to certain regions, instruments, or extreme weather events. We investigate the application of modern domain-adaptation to multiple proposed geospatial benchmarks, uncovering unique challenges and proposing solutions to them. Authors: John M Lynch (NC State University); Sam Wookey (Masterful AI)	Unsupervised & Semi-Supervised Learning Ecosystems & Biodiversity Computer Vision & Remote Sensing
ICML 2021	APPLYING TRANSFORMER TO IMPUTATION OF MULTI-VARIATE ENERGY TIME SERIES DATA (Proposals Track) Abstract and authors: (click to expand) Abstract: To reduce the greenhouse gas emissions from electricity production, it is necessaryto switch to an energy system based on renewable energy sources (RES). However,intermittent electricity generation from RES poses challenges for energy systems.The primary input for data-driven solutions is data on electricity generation fromRES, which usually contain many missing values. This proposal studies the useof attention-based algorithms to impute missing values of electricity production,electricity demand and electricity prices. Since attention mechanisms allow us totake into account dependencies between time series across multiple dimensionsefficiently, our approach goes beyond classic statistical methods and incorporatesmany related variables, such as electricity price, demand and production by othersources. Our preliminary results show that while transformers can come at highercomputational costs, they are more precise than classical imputation methods. Authors: Hasan Ümitcan Yilmaz (Karlsruhe Institute of Technology); Max Kleinebrahm (Karlsruhe Institut für Technologie); Christopher Bülte (Karlsruhe Institute of Technology); Juan Gómez-Romero (Universidad de Granada)	Power & Energy Time-series Analysis Unsupervised & Semi-Supervised Learning
NeurIPS 2020	Electric Vehicle Range Improvement by Utilizing Deep Learning to Optimize Occupant Thermal Comfort (Papers Track) Abstract and authors: (click to expand) Abstract: Heating, ventilation and air-conditioning (HVAC) systems can have a significant impact on the driving range of battery electric vehicles (EV’s). Predicting thermal comfort in an automotive vehicle cabin’s highly asymmetric and dynamic thermal environment is critical for developing energy-efficient HVAC systems. In this study we have coupled high-fidelity Computational Fluid Dynamics (CFD) simulations and Artificial Neural Networks (ANN) to predict vehicle occupant thermal comfort for any combination of steady-state boundary conditions. A vehicle cabin CFD model, validated against climatic wind tunnel measurements, was used to systematically generate training and test data that spanned the entire range of boundary conditions which impact occupant thermal comfort in an electric vehicle. Artificial neural networks (ANN) were applied to the simulation data to predict the overall Equivalent Homogeneous Temperature (EHT) comfort index for each occupant. An ensemble of five neural network models was able to achieve a mean absolute error of 2 ºC or less in predicting the overall EHT for all occupants in the vehicle on unseen or test data, which is acceptable for rapid evaluation and optimization of thermal comfort energy demand. The deep learning model developed in this work enables predictions of thermal comfort for any combination of steady-state boundary conditions in real-time without being limited by time-consuming and expensive CFD simulations or climatic wind tunnel tests. This model has been deployed as an easy-to-use web application within the organization for HVAC engineers to optimize thermal comfort energy demand and, thereby, driving range of electric vehicle programs. Authors: Alok Warey (General Motors Global Research and Development); Shailendra Kaushik (General Motors Global Research and Development); Bahram Khalighi (General Motors Global Research and Development); Michael Cruse (Siemens Digital Industries Software); Ganesh Venkatesan (Siemens Digital Industries Software)	Transportation
NeurIPS 2020	Is Africa leapfrogging to renewables or heading for carbon lock-in? A machine-learning-based approach to predicting success of power-generation projects (Papers Track) Abstract and authors: (click to expand) Abstract: Several extant energy-planning studies, comprising wide-ranging assumptions about the future, feature projections of Africa’s rapid transition in the next decade towards renewables-based power generation. Here, we develop a novel empirical approach to predicting medium-term generation mix that can complement traditional energy planning. Relying on the largest dataset on historic and planned power plants available for Africa, combined with country-level characteristics, we build a machine-learning-based model, using gradient boosted trees, that demonstrates high predictive performance. Training our model on past successful and failed projects, we find that the most relevant factors for commissioning are plant-level: capacity, fuel, ownership and grid connection type. We then apply the trained model to predict the realisation of the current project pipeline. Contrary to the rapid transition scenarios, our results show that the share of non-hydro renewables in generation is likely to remain below 10% in 2030, despite total generation more than doubling. These findings point to high carbon lock-in risks in Africa, highlighting the urgency to shift its pipeline of projects towards low-carbon energy and improve the realisation chances of renewable energy plants. Authors: Galina Alova (University of Oxford); Philipp Trotter (University of Oxford); Alex Money (University of Oxford)	Power & Energy Climate Finance & Economics Interpretable ML
NeurIPS 2020	pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research (Papers Track) Abstract and authors: (click to expand) Abstract: Microgrids – self-contained electrical grids that are capable of disconnecting from the main grid – hold potential in both tackling climate change mitigation via reducing CO$_2$ emissions and adaptation by increasing infrastructure resiliency. Due to their distributed nature, microgrids are often idiosyncratic; as a result, control of these systems is nontrivial. While microgrid simulators exist, many are limited in scope and in the variety of microgrids they can simulate. We propose \HL{pymgrid}, an open-source Python package to generate and simulate a large number of microgrids, and the first open-source tool that can generate more than 600 different microgrids. \HL{pymgrid} abstracts most of the domain expertise, allowing users to focus on control algorithms. In particular, \HL{pymgrid} is built to be a reinforcement learning (RL) platform, and includes the ability to model microgrids as Markov decision processes. \HL{pymgrid} also introduces two pre-computed list of microgrids, intended to allow for research reproducibility in the microgrid setting. Authors: Gonzague Henri (Total); Tanguy Levent (Ecole Polytechnique); Avishai Halev (Total, UC Davis); Reda ALAMI (Total R&D); Philippe Cordier (Total S.A.)	Power & Energy Reinforcement Learning
NeurIPS 2020	Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Achieving efficiency gains in Chinese district heating networks, thereby reducing their carbon footprint, requires new optimal control methods going beyond current industry tools. Focusing on the secondary network, we propose a data-driven deep reinforcement learning (DRL) approach to address this task. We build a recurrent neural network, trained on simulated data, to predict the indoor temperatures. This model is then used to train two DRL agents, with or without expert guidance, for the optimal control of the supply water temperature. Our tests in a multi-apartment setting show that both agents can ensure a higher thermal comfort and at the same time a smaller energy cost, compared to an optimized baseline strategy. Authors: Adrien Le Coz (EDF); Tahar Nabil (EDF); Francois Courtot (EDF)	Buildings Reinforcement Learning
NeurIPS 2020	Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs (Papers Track) Abstract and authors: (click to expand) Abstract: A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change. Decarbonization of the electricity grid would allow for low-carbon heating, cooling and transport. Investments in renewable energy must be made over a long time horizon to maximise return of investment of these long life power generators. Over these long time horizons, there exist multiple uncertainties, for example in future electricity demand and costs to consumers and investors. To mitigate for imperfect information of the future, we use the deep deterministic policy gradient (DDPG) deep reinforcement learning approach to optimize for a low-cost, low-carbon electricity supply using a modified version of the FTT:Power model. In this work, we model the UK and Ireland electricity markets. The DDPG algorithm is able to learn the optimum electricity mix through experience and achieves this between the years of 2017 and 2050. We find that a change from fossil fuels and nuclear power to renewables, based upon wind, solar and wave would provide a cheap and low-carbon alternative to fossil fuels. Authors: Alexander J. M. Kell (Newcastle University); Pablo Salas (University of Cambridge); Jean-Francois Mercure (University of Exeter); Matthew Forshaw (Newcastle University); A. Stephen McGough (Newcastle University)	Power & Energy Reinforcement Learning
NeurIPS 2020	Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models (Papers Track) Abstract and authors: (click to expand) Abstract: Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting. Authors: Eric Zelikman (Stanford University); Sharon Zhou (Stanford University); Jeremy A Irvin (Stanford); Cooper Raterink (Stanford University); Hao Sheng (Stanford University); Avati Anand (Stanford University); Jack Kelly (Open Climate Fix); Ram Rajagopal (Stanford University); Andrew Ng (Stanford University); David J Gagne (National Center for Atmospheric Research)	Power & Energy Climate Science & Modeling Causal & Bayesian Methods Uncertainty Quantification & Robustness
NeurIPS 2020	Deep Learning for Climate Model Output Statistics (Papers Track) Best ML Innovation Abstract and authors: (click to expand) Abstract: Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches. Authors: Michael Steininger (University of Würzburg); Daniel Abel (University of Würzburg); Katrin Ziegler (University of Würzburg); Anna Krause (Universität Würzburg, Department of Computer Science, CHair X Data Science); Heiko Paeth (University of Würzburg); Andreas Hotho (Universitat Wurzburg)	Climate Science & Modeling
NeurIPS 2020	A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications (Papers Track) Abstract and authors: (click to expand) Abstract: Improving irradiance forecasting is critical to further increase the share of solar in the energy mix. On a short time scale, fish-eye cameras on the ground are used to capture cloud displacements causing the local variability of the electricity production. As most of the solar radiation comes directly from the Sun, current forecasting approaches use its position in the image as a reference to interpret the cloud cover dynamics. However, existing Sun tracking methods rely on external data and a calibration of the camera, which requires access to the device. To address these limitations, this study introduces an image-based Sun tracking algorithm to localise the Sun in the image when it is visible and interpolate its daily trajectory from past observations. We validate the method on a set of sky images collected over a year at SIRTA's lab. Experimental results show that the proposed method provides robust smooth Sun trajectories with a mean absolute error below 1% of the image size. Authors: Quentin Paletta (University of Cambridge); Joan Lasenby (University of Cambridge)	Power & Energy Computer Vision & Remote Sensing Time-series Analysis
NeurIPS 2020	Characterization of Industrial Smoke Plumes from Remote Sensing Data (Papers Track) Abstract and authors: (click to expand) Abstract: The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth’s climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA’s Sentinel-2 satellites. Using a modified ResNet-50, we can detect smoke plumes of different sizes with an accuracy of 94.3%. The model correctly ignores natural clouds and focuses on those imaging channels that are related to the spectral absorption from aerosols and water vapor, enabling the localization of smoke. We exploit this localization ability and train a U-Net segmentation model on a labeled sub-sample of our data, resulting in an Intersection-over-Union (IoU) metric of 0.608 and an overall accuracy for the detection of any smoke plume of 94.0%; on average, our model can reproduce the area covered by smoke in an image to within 5.6%. The performance of our model is mostly limited by occasional confusion with surface objects, the inability to identify semi-transparent smoke, and human limitations to properly identify smoke based on RGB-only images. Nevertheless, our results enable us to reliably detect and qualitatively estimate the level of smoke activity in order to monitor activity in industrial plants across the globe. Our data set and code base are publicly available. Authors: Michael Mommert (University of St. Gallen); Mario Sigel (Sociovestix Labs Ltd.); Marcel Neuhausler (ISS Technology Innovation Lab); Linus M. Scheibenreif (University of St. Gallen); Damian Borth (University of St. Gallen)	Computer Vision & Remote Sensing Heavy Industry and Manufacturing
NeurIPS 2020	Learning the distribution of extreme precipitation from atmospheric general circulation model variables (Papers Track) Abstract and authors: (click to expand) Abstract: Precipitation extremes are projected to become more frequent and severe in a warming atmosphere over the coming decades. However, the accurate prediction of precipitation, in particular of extremes, remains a challenge for numerical weather prediction models. A large source of error are subgrid-scale parameterizations of processes that play a crucial role in the complex, multi-scale dynamics of precipitation, but are not explicitly resolved in the model formulation. Here we follow a hybrid, data-driven approach, in which atmospheric variables such as wind fields are forecast in time by a general circulation model (GCM) ensemble and then mapped to precipitation using a deep convolutional autoencoder. A frequency-based weighting of the loss function is introduced to improve the learning with regard to extreme values. Our results show an improved representation of extreme precipitation frequencies, as well as better error and correlation statistics compared to a state-of-the-art GCM ensemble. Authors: Philipp Hess (Free University Berlin); Niklas Boers (Free University Berlin)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2020	Towards Tracking the Emissions of Every Power Plant on the Planet (Papers Track) Best Pathway to Impact Abstract and authors: (click to expand) Abstract: Greenhouse gases emitted from fossil-fuel-burning power plants are a major contributor to climate change. Current methods to track emissions from individual sources are expensive and only used in a few countries. While carbon dioxide concentrations can be measured globally using remote sensing, background fluctuations and low spatial resolution make it difficult to attribute emissions to individual sources. We use machine learning to infer power generation and emissions from visible and thermal power plant signatures in satellite images. By training on a data set of power plants for which we know the generation or emissions, we are able to apply our models globally. This paper demonstrates initial progress on this project by predicting whether a power plant is on or off from a single satellite image. Authors: Heather D Couture (Pixel Scientia Labs); Joseph O'Connor (Carbon Tracker); Grace Mitchell (WattTime); Isabella Söldner-Rembold (Carbon Tracker); Durand D'souza (Carbon Tracker); Krishna Karra (WattTime); Keto Zhang (WattTime); Ali Rouzbeh Kargar (WattTime); Thomas Kassel (WattTime); Brian Goldman (Google); Daniel Tyrrell (Google); Wanda Czerwinski (Google); Alok Talekar (Google); Colin McCormick (Georgetown University)	Power & Energy Computer Vision & Remote Sensing
NeurIPS 2020	Spatio-Temporal Learning for Feature Extraction inTime-Series Images (Papers Track) Abstract and authors: (click to expand) Abstract: Earth observation programs have provided highly useful information in global climate change research over the past few decades and greatly promoted its development, especially through providing biological, physical, and chemical parameters on a global scale. Programs such as Landsat, Sentinel, SPOT, and Pleiades can be used to acquire huge volume of medium to high resolution images every day. In this work, we organize these data in time series and we exploit both temporal and spatial information they provide to generate accurate and up-to-date land cover maps that can be used to monitor vulnerable areas threatened by the ongoing climatic and anthropogenic global changes. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are described. Examples are provided for the monitoring of roads and mangrove forests on the West African coast. Authors: Gael Kamdem De Teyou (Huawei)	Ecosystems & Biodiversity Forests Land Use
NeurIPS 2020	Meta-modeling strategy for data-driven forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Accurately forecasting the weather is a key requirement for climate change mitigation. Data-driven methods offer the ability to make more accurate forecasts, but lack interpretability and can be expensive to train and deploy if models are not carefully developed. Here, we make use of two historical climate data sets and tools from machine learning, to accurately predict temperature fields. Furthermore, we are able to use low fidelity models that are cheap to train and evaluate, to selectively avoid expensive high fidelity function evaluations, as well as uncover seasonal variations in predictive power. This allows for an adaptive training strategy for computationally efficient geophysical emulation. Authors: Dominic J Skinner (MIT); Romit Maulik (Argonne National Laboratory)	Climate Science & Modeling Interpretable ML Time-series Analysis
NeurIPS 2020	Short-term prediction of photovoltaic power generation using Gaussian process regression (Papers Track) Abstract and authors: (click to expand) Abstract: Photovoltaic (PV) power is affected by weather conditions, making the power generated from the PV systems uncertain. Solving this problem would help improve the reliability and cost effectiveness of the grid, and could help reduce reliance on fossil fuel plants. The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom using Gaussian process regression (GPR). Gaussian process regression is a Bayesian non-parametric model that can provide predictions along with the uncertainty in the predicted value, which can be very useful in applications with a high degree of uncertainty. The model is evaluated for short-term forecasts of 48 hours against three main factors – training period, sky area coverage and kernel model selection – and for very short-term forecasts of four hours against sky area. We also compare very short-term forecasts in terms of cloud coverage within the prediction period and only initial cloud coverage as a predictor. Authors: Yahya Hasan Al Lawati (Queen Mary University of London); Jack Kelly (Open Climate Fix); Dan Stowell (Queen Mary University of London)	Causal & Bayesian Methods
NeurIPS 2020	Formatting the Landscape: Spatial conditional GAN for varying population in satellite imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is expected to reshuffle the settlement landscape: forcing people in affected areas to migrate, to change their lifeways, and continuing to affect demographic change throughout the world. Changes to the geographic distribution of population will have dramatic impacts on land use and land cover and thus constitute one of the major challenges of planning for climate change scenarios. In this paper, we explore a generative model framework for generating satellite imagery conditional on gridded population distributions. We make additions to the existing ALAE [30] architecture, creating a spatially conditional version: SCALAE. This method allows us to explicitly disentangle population from the model’s latent space and thus input custom population forecasts into the generated imagery. We postulate that such imagery could then be directly used for land cover and land use change estimation using existing frameworks, as well as for realistic visualisation of expected local change. We evaluate the model by comparing pixel and semantic reconstructions, as well as calculate the standard FID metric. The results suggest the model captures population distributions accurately and delivers a controllable method to generate realistic satellite imagery. Authors: Tomas Langer (Intuition Machines); Natalia Fedorova (Max Planck Institute for Evolutionary Anthropology); Ron Hagensieker (Osir.io)	Generative Modeling Behavioral and Social Science Buildings Societal Adaptation & Resilience Computer Vision & Remote Sensing
NeurIPS 2020	Storing Energy with Organic Molecules: Towards a Metric for Improving Molecular Performance for Redox Flow Batteries (Papers Track) Abstract and authors: (click to expand) Abstract: Energy storage is an important tool in the decarbonization of energy systems, particularly when coupled with intermittent forms of energy. However, storage technologies are still not commercially competitive to garner mainstream adoption. This work focuses on the cost reduction of organic redox flow batteries (ORFBs) via materials discovery. We identify macroscopic metrics of interest to optimize for lowering their cost and relate them to the molecular properties of the materials involved. Furthermore, we consolidate a benchmark set of experimental data for building predictive models for these materials properties. Building more accurate models will aid practitioners in the rational design of new ORFB. Authors: Luis M Mejia Mendoza (University of Toronto); Alan Aspuru-Guzik (Harvard University); Martha Flores Leonar (University of Toronto)	Meta- and Transfer Learning
NeurIPS 2020	Predicting Landsat Reflectance with Deep Generative Fusion (Papers Track) Abstract and authors: (click to expand) Abstract: Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution as no single sensor provides fine-grained acquisitions with frequent coverage. This hinders their potential to assist vegetation monitoring or humanitarian actions, which require detecting rapid and detailed terrestrial surface changes. In this work, we probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics. We introduce a dataset of co-registered Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat surface reflectance time series and demonstrate the ability of our generative model to blend coarse daily reflectance information into low-paced finer acquisitions. We benchmark our proposed model against state-of-the-art reflectance fusion algorithms. Authors: Shahine Bouabid (University of Oxford); Jevgenij Gamper (Cervest Ltd.)	Generative Modeling Computer Vision & Remote Sensing Forests Land Use
NeurIPS 2020	Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter (Papers Track) Abstract and authors: (click to expand) Abstract: Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not identified well yet because of the complexity and data scarcity. In this paper, we proposed a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts and connected a typical drought indicator, Standardized Precipitation Index (SPI), to the text-based impacts from the Drought Impact Reporter (DIR). The preliminary results of this study showed an outstanding performance of the well-trained models to assess drought impacts on agriculture, fire, society & public health, plants & wildlife, as well as relief, response & restrictions in Texas. It also provided a possibility to appraise drought impacts using hydro-meteorological indicators with the proposed framework in the United States, which could help drought risk management by giving additional information and improving the updating frequency of drought impacts. Our interpretation results using the Shapley additive explanation (SHAP) interpretability technique revealed that the rules guiding the predictions of XGBoost comply with domain expertise knowledge around the role that SPI indicators play around drought impacts. Authors: Beichen Zhang (University of Nebraska-Lincoln); Fatima K Abu Salem (American University of Beirut); Michael Hayes (University of Nebraska-Lincoln); Tsegaye Tadesse (University of Nebraska-Lincoln)	Disaster Management and Relief Interpretable ML
NeurIPS 2020	Estimating Forest Ground Vegetation Cover From Nadir Photographs Using Deep Convolutional Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Forest fires, such as those on the US west coast in September 2020, are an important factor in climate change. Wildfire modeling and mitigation require mapping vegetation ground cover over large plots of land. The current forestry practice is to send out human ground crews to collect photos of the forest floor at precisely determined locations, then manually calculate the percent cover of ground fuel types. In this work, we propose automating this process using a supervised learning-based deep convolutional neural network to perform image segmentation. Experimental results on a real dataset show this approach delivers very promising performance. Authors: Pranoy Panda (Indian Institute of Technology, Hyderabad); Martin Barczyk (University of Alberta); Jen Beverly (University of Alberta)	Forests
NeurIPS 2020	A Machine Learning Approach to Methane Emissions Mitigation in the Oil and Gas Industry (Papers Track) Overall Best Paper Abstract and authors: (click to expand) Abstract: Reducing methane emissions from the oil and gas sector is a key component of climate policy in the United States. Methane leaks across the supply chain are stochastic and intermittent, with a small number of sites (‘super-emitters’) responsible for a majority of emissions. Thus, cost-effective emissions reduction critically relies on effectively identifying the super-emitters from thousands of well-sites and millions of miles of pipelines. Conventional approaches such as walking surveys using optical gas imaging technology are slow and time-consuming. In addition, several variables contribute to the formation of leaks such as infrastructure age, production, weather conditions, and maintenance practices. Here, we develop a machine learning algorithm to predict high-emitting sites that can be prioritized for follow-up repair. Such prioritization can significantly reduce the cost of surveys and increase emissions reductions compared to conventional approaches. Our results show that the algorithm using logistic regression performs the best out of several algorithms. The model achieved a 70% accuracy rate with a 57% recall and a 66% balanced accuracy rate. Compared to the conventional approach, the machine learning model reduced the time to achieve a 50% emissions mitigation target by 42%. Correspondingly, the mitigation cost reduced from $85/t CO2e to $49/t CO2e. Authors: Jiayang Wang (Harrisburg University); Selvaprabu Nadarajah (University of Illinois at Chicago); Jingfan Wang (Stanford University); Arvind Ravikumar (Harrisburg University)	Power & Energy
NeurIPS 2020	Monitoring the Impact of Wildfires on Tree Species with Deep Learning (Papers Track) Abstract and authors: (click to expand) Abstract: One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species. Here a deep learning model is customized to classify land covers from four-band aerial imagery before and after wildfires to study the prolonged consequences of wildfires on tree species. The tree species labels are generated from manually delineated maps for five land cover classes: Conifer, Hardwood, Shrub, ReforestedTree, and Barren land. With an accuracy of 92% on the test split, the model is applied to three wildfires on data from 2009 to 2018. The model accurately delineates areas damaged by wildfires, changes in tree species, and regrowth in burned areas. The result shows clear evidence of wildfires impacting the local ecosystem and the outlined approach can help monitor reforested areas, observe changes in forest composition, and track wildfire impact on tree species. Authors: Wang Zhou (IBM Research); Levente Klein (IBM Research)	Disaster Management and Relief Computer Vision & Remote Sensing Forests
NeurIPS 2020	ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpreters. We use the dataset to train and validate the models and demonstrate that ForestNet substantially outperforms other standard driver classification approaches. In order to support future research on automated approaches to deforestation driver classification, the dataset curated in this study is publicly available at https://stanfordmlgroup.github.io/projects/forestnet . Authors: Jeremy A Irvin (Stanford); Hao Sheng (Stanford University); Neel Ramachandran (Stanford University); Sonja Johnson-Yu (Stanford University); Sharon Zhou (Stanford University); Kyle Story (Descartes Labs); Rose Rustowicz (Descartes Labs); Cooper Elsworth (Descartes Labs); Kemen Austin (RTI International); Andrew Ng (Stanford University)	Forests Land Use Computer Vision & Remote Sensing
NeurIPS 2020	Mangrove Ecosystem Detection using Mixed-Resolution Imagery with a Hybrid-Convolutional Neural Network (Papers Track) Abstract and authors: (click to expand) Abstract: Mangrove forests are rich in biodiversity and are a large contributor to carbon sequestration critical in the fight against climate change. However, they are currently under threat from anthropogenic activities, so monitoring their health, extent, and productivity is vital to our ability to protect these important ecosystems. Traditionally, lower resolution satellite imagery or high resolution unmanned air vehicle (UAV) imagery has been used independently to monitor mangrove extent, both offering helpful features to predict mangrove extent. To take advantage of both of these data sources, we propose the use of a hybrid neural network, which combines a Convolutional Neural Network (CNN) feature extractor with a Multilayer-Perceptron (MLP), to accurately detect mangrove areas using both medium resolution satellite and high resolution drone imagery. We present a comparison of our novel Hybrid CNN with algorithms previously applied to mangrove image classification on a data set we collected of dwarf mangroves from consumer UAVs in Baja California Sur, Mexico, and show a 95\% intersection over union (IOU) score for mangrove image classification, outperforming all our baselines. Authors: Dillon Hicks (Engineers for Exploration); Ryan Kastner (University of California San Diego); Curt Schurgers (University of California San Diego); Astrid Hsu (University of California San Diego); Octavio Aburto (University of California San Diego)	Computer Vision & Remote Sensing Carbon Capture & Sequestration Ecosystems & Biodiversity Forests
NeurIPS 2020	Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models (Papers Track) Abstract and authors: (click to expand) Abstract: Buildings produce more U.S. greenhouse gas emissions through electricity generation than any other economic sector. To improve the energy efficiency of buildings, engineers often rely on physics-based building simulations to predict the impacts of retrofits in individual buildings. In dense urban areas, these models suffer from inaccuracy due to imprecise parameterization or external, unmodeled urban context factors such as inter-building effects and urban microclimates. In a case study of approximately 30 buildings in Sacramento, California, we demonstrate how our hybrid physics-driven deep learning framework can use these external factors advantageously to identify a more optimal energy efficiency retrofit installation strategy and achieve significant savings in both energy and cost. Authors: Benjamin Choi (Stanford University); Alex Nutkiewicz (Stanford University); Rishee Jain (Stanford University)	Hybrid Physical Models Buildings
NeurIPS 2020	Deep learning architectures for inference of AC-OPF solutions (Papers Track) Abstract and authors: (click to expand) Abstract: We present a systematic comparison between neural network (NN) architectures for inference of AC-OPF solutions. Using fully connected NNs as a baseline we demonstrate the efficacy of leveraging network topology in the models by constructing abstract representations of electrical grids in the graph domain, for both convolutional and graph NNs. The performance of the NN architectures is compared for regression (predicting optimal generator set-points) and classification (predicting the active set of constraints) settings. Computational gains for obtaining optimal solutions are also presented. Authors: Thomas Falconer (University College London); Letif Mones (Invenia Labs)	Power & Energy
NeurIPS 2020	Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data (Papers Track) Abstract and authors: (click to expand) Abstract: Estimating the amount of electricity that can be produced by rooftop photovoltaic systems is a time-consuming process that requires on-site measurements, a difficult task to achieve on a large scale. In this paper, we present an approach to estimate the solar potential of rooftops based on their location and architectural characteristics, as well as the amount of solar radiation they receive annually. Our technique uses computer vision to achieve semantic segmentation of roof sections and roof objects on the one hand, and a machine learning model based on structured building features to predict roof pitch on the other hand. We then compute the azimuth and maximum number of solar panels that can be installed on a rooftop with geometric approaches. Finally, we compute precise shading masks and combine them with solar irradiation data that enables us to estimate the yearly solar potential of a rooftop. Authors: Daniel de Barros Soares (nam.R); François ANDRIEUX (nam.R); Bastien HELL (nam.R); Julien LENHARDT (nam.R; ENSTA); JORDI BADOSA (Ecole Polytechnique); Sylvain GAVOILLE (nam.R); Stéphane GAIFFAS (nam.R; LPSM (Université de Paris)); Emmanuel BACRY (nam.R; CEREMADE (Université Paris Dauphine, PSL))	Buildings Power & Energy Computer Vision & Remote Sensing
NeurIPS 2020	Revealing the Oil Majors' Adaptive Capacity to the Energy Transition with Deep Multi-Agent Reinforcement Learning (Papers Track) Abstract and authors: (click to expand) Abstract: A low-carbon energy transition is transpiring to combat climate change, posing an existential threat to oil and gas companies, particularly the Majors. Though Majors yield the resources and expertise to adapt to low-carbon business models, meaningful climate-aligned strategies have yet to be enacted. A 2-degrees pathways (2DP) wargame was developed to assess climate-compatible pathways for the oil Majors. Recent advances in deep multi-agent reinforcement learning (MARL) have achieved superhuman-level performance in solving high-dimensional continuous control problems. Modeling within a Markovian framework, we present the novel 2DP-MARL model which applies deep MARL methods to solve the 2DP wargame across a multitude of transition scenarios. Designed to best mimic Majors in real- life competition, the model reveals all Majors quickly adapt to low-carbon business models to remain robust amidst energy transition uncertainty. The purpose of this work is provide tangible metrics to support the call for oil Majors to diversify into low-carbon business models and, thus, accelerate the energy transition. Authors: Dylan Radovic (Imperial College London); Lucas Kruitwagen (University of Oxford); Christian Schroeder de Witt (University of Oxford)	Heavy Industry and Manufacturing Climate Finance & Economics Reinforcement Learning Uncertainty Quantification & Robustness
NeurIPS 2020	Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution (Papers Track) Abstract and authors: (click to expand) Abstract: Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground-level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities. Authors: Matteo Bohm (Sapienza University of Rome); Mirco Nanni (ISTI-CNR Pisa, Italy); Luca Pappalardo (ISTI)	Transportation Data Mining
NeurIPS 2020	Annual and in-season mapping of cropland at field scale with sparse labels (Papers Track) Abstract and authors: (click to expand) Abstract: Spatial information about where crops are being grown, known as cropland maps, are critical inputs for analyses and decision-making related to food security and climate change. Despite a widespread need for readily-updated annual and in-season cropland maps at the management (field) scale, these maps are unavailable for most regions at risk of food insecurity. This is largely due to lack of in-situ labels for training and validating machine learning classifiers. Previously, we developed a method for binary classification of cropland that learns from sparse local labels and abundant global labels using a multi-headed LSTM and time-series multispectral satellite inputs over one year. In this work, we present a new method that uses an autoregressive LSTM to classify cropland during the growing season (i.e., partially-observed time series). We used these methods to produce publicly-available 10m-resolution cropland maps in Kenya for the 2019-2020 and 2020-2021 growing seasons. These are the highest-resolution and most recent cropland maps publicly available for Kenya. These methods and associated maps are critical for scientific studies and decision-making at the intersection of food security and climate change. Authors: Gabriel Tseng (NASA Harvest); Hannah R Kerner (University of Maryland); Catherine L Nakalembe (University of Maryland); Inbal Becker-Reshef (University of Maryland)	Agriculture & Food Computer Vision & Remote Sensing
NeurIPS 2020	NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations (Papers Track) Abstract and authors: (click to expand) Abstract: The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night. The gap can be filled by employing available infra-red observations to generate visible images. This work presents how Deep Learning can be applied successfully to create those images by using U-Net based architectures. The proposed methods show promising results, achieving a structural similarity index (SSIM) up to 86\% on an independent test set and providing visually convincing output images, generated from infra-red observations. Authors: Paula Harder (Fraunhofer ITWM); William Jones (University of Oxford); Redouane Lguensat (LSCE-IPSL); Shahine Bouabid (University of Oxford); James Fulton (University of Edinburgh); Dánnell Quesada-Chacón (Technische Universität Dresden); Aris Marcolongo (University of Bern); Sofija Stefanovic (University of Oxford); Yuhan Rao (North Carolina Institute for Climate Studies); Peter Manshausen (University of Oxford); Duncan Watson-Parris (University of Oxford)	Computer Vision & Remote Sensing Climate Science & Modeling Generative Modeling
NeurIPS 2020	Analyzing Sustainability Reports Using Natural Language Processing (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is a far-reaching, global phenomenon that will impact many aspects of our society, including the global stock market. In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt their practices the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of sustainability measures, often under the umbrella of Environmental, Social, and Governance (ESG) disclosures. However, given this abundance of data, sustainability analysts are obliged to comb through hundreds of pages of reports in order to find relevant information. We have leveraged recent progress in Natural Language Processing (NLP) to create a custom model, ClimateQA, which allows the analysis of financial reports in order to identify climate-relevant sections using a question answering approach. We present this tool and the methodology that we used to develop it in the present article. Authors: Sasha Luccioni (Mila); Emi Baylor (McGill); Nicolas Duchene (Universite de Montreal)	Climate Finance & Economics Natural Language Processing
NeurIPS 2020	Automated Identification of Oil Field Features using CNNs (Papers Track) Abstract and authors: (click to expand) Abstract: Oil and gas production sites have been identified as a major source of anthropogenic methane emissions. Emissions studies utilize counts of equipment to estimate emissions from production facilities. However these counts are poorly documented, including both information about well pad locations and major equipment on each well pad. While these data can be found by manually reviewing satellite imagery, it is prohibitively difficult and time consuming. This work, part of a larger study of methane emission studies in Colorado, US, adapted a machine learning (ML) algorithm to detect well pads and associated equipment. Our initial model showed an average well pad detection accuracy of 95% on the Denver-Julesburg (DJ) basin in northeastern Colorado. Our example demonstrates the potential for this type of automated detection from satellite imagery, leading to more accurate and complete models of production emissions. Authors: SONU DILEEP (Colorado State University); Daniel Zimmerle (Colorado State University); Ross Beveridge (CSU); Timothy Vaughn (Colorado State University)	Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2020	Using attention to model long-term dependencies in occupancy behavior (Papers Track) Abstract and authors: (click to expand) Abstract: Over the past years, more and more models have been published that aim to capture relationships in human residential behavior. Most of these models are different Markov variants or regression models that have a strong assumption bias and are therefore unable to capture complex long-term dependencies and the diversity in occupant behavior. This work shows that attention based models are able to capture complex long-term dependencies in occupancy behavior and at the same time adequately depict the diversity in behavior across the entire population and different socio-demographic groups. By combining an autoregressive generative model with an imputation model, the advantages of two data sets are combined and new data are generated which are beneficial for multiple use cases (e.g. generation of consistent household energy demand profiles). The two step approach generates synthetic activity schedules that have similar statistical properties as the empirical collected schedules and do not contain direct information about single individuals. Therefore, the presented approach forms the basis to make data on occupant behavior freely available, so that further investigations based on the synthetic data can be carried out without a large data application effort. In future work it is planned to take interpersonal dependencies into account in order to be able to generate entire household behavior profiles. Authors: Max Kleinebrahm (Karlsruhe Institut für Technologie); Jacopo Torriti (University Reading); Russell McKenna (University of Aberdeen); Armin Ardone (Karlsruhe Institut für Technologie); Wolf Fichtner (Karlsruhe Institute of Technology)	Behavioral and Social Science Buildings Power & Energy Transportation Natural Language Processing Time-series Analysis
NeurIPS 2020	Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation (Papers Track) Abstract and authors: (click to expand) Abstract: To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground truth data into the segmentation of satellite imagery of floods. We propose this approach as a solution to the labor-intensive task of generating high-quality, hand-labeled training data, and demonstrate successes and failures of different plausible crowdsourcing approaches in our model. Street to Cloud leverages community reporting and machine learning to generate novel, near-real time insights into the extent of floods to be used for emergency response. Authors: Veda Sunkara (Cloud to Street); Matthew Purri (Rutgers University); Bertrand Le Saux (European Space Agency (ESA)); Jennifer Adams (European Space Agency (ESA))	Disaster Management and Relief Computer Vision & Remote Sensing
NeurIPS 2020	Accurate river level predictions using a Wavenet-like model (Papers Track) Abstract and authors: (click to expand) Abstract: The effects of climate change on river levels are noticeable through a higher occurrence of floods with disastrous social and economic impacts. As such, river level forecasting is essential in flood mitigation, infrastructure management and secure shipping. Historical records of river levels and influencing factors such as rainfall or soil conditions are used for predicting future river levels. The current state-of-the-art time-series prediction model is the LSTM network, a recurrent neural network. In this work we study the efficiency of convolutional models, and specifically the WaveNet model in forecasting one-day ahead river levels. We show that the additional benefit of the WaveNet model is the computational ease with which other input features can be included in the predictions of river stage and river flow. The conditional WaveNet models outperformed conditional LSTM models for river level prediction by capturing short-term, non-linear dependencies between input data. Furthermore, the Wavenet model offers a faster computation time, stable results and more possibilities for fine-tuning. Authors: Shannon Doyle (UvA); Anastasia Borovykh (Imperial College London)	Disaster Management and Relief
NeurIPS 2020	Movement Tracks for the Automatic Detection of Fish Behavior in Videos (Papers Track) Abstract and authors: (click to expand) Abstract: Global warming is predicted to profoundly impact ocean ecosystems. Fish behavior is an important indicator of changes in such marine environments. Thus, the automatic identification of key fish behavior in videos represents a much needed tool for marine researchers, enabling them to study climate change-related phenomena. We offer a dataset of sablefish (Anoplopoma fimbria) startle behaviors in underwater videos, and investigate the use of deep learning (DL) methods for behavior detection on it. Our proposed detection system identifies fish instances using DL-based frameworks, determines trajectory tracks, derives novel behavior-specific features, and employs Long Short-Term Memory (LSTM) networks to identify startle behavior in sablefish. Its performance is studied by comparing it with a state-of-the-art DL-based video event detector. Authors: Declan GD McIntosh (University Of Victoria); Tunai Porto Marques (University of Victoria); Alexandra Branzan Albu (University of Victoria); Rodney Rountree (University of Victoria); Fabio De Leo Cabrera (Ocean Networks Canada)	Computer Vision & Remote Sensing Ecosystems & Biodiversity Time-series Analysis
NeurIPS 2020	Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Cattle farming is responsible for 8.8\% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive process, the growing need for grazing areas is an important driver of deforestation. While some regulations are in place for preserving the Amazon against deforestation, these are being flouted in various ways. Hence the need to scale and automate the monitoring of cattle ranching activities. Through a partnership with \textit{Anonymous under review}, we explore the feasibility of tracking and counting cattle at the continental scale from satellite imagery. With a license from Maxar Technologies, we obtained satellite imagery of the Amazon at 40cm resolution, and compiled a dataset of 903 images containing a total of 28498 cattle. Our experiments show promising results and highlight important directions for the next steps on both counting algorithms and the data collection processes for solving such challenges. Authors: Issam Hadj Laradji (Element AI); Pau Rodriguez (Element AI); Alfredo Kalaitzis (University of Oxford); David Vazquez (Element AI); Ross Young (Element AI); Ed Davey (Global Witness); Alexandre Lacoste (Element AI)	Computer Vision & Remote Sensing Climate Science & Modeling Ecosystems & Biodiversity Agriculture & Food Forests
NeurIPS 2020	RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is expected to aggravate extreme precipitation events, directly impacting the livelihood of millions. Without a global precipitation forecasting system in place, many regions -- especially those constrained in resources to collect expensive groundstation data -- are left behind. To mitigate such unequal reach of climate change, a solution is to alleviate the reliance on numerical models (and by extension groundstation data) by enabling machine-learning-based global forecasts from satellite imagery. Though prior works exist in regional precipitation nowcasting, there lacks work in global, medium-term precipitation forecasting. Importantly, a common, accessible baseline for meaningful comparison is absent. In this work, we present RainBench, a multi-modal benchmark dataset dedicated to advancing global precipitation forecasting. We establish baseline tasks and release PyRain, a data-handling pipeline to enable efficient processing of decades-worth of data by any modeling framework. Whilst our work serves as a basis for a new chapter on global precipitation forecast from satellite imagery, the greater promise lies in the community joining forces to use our released datasets and tools in developing machine learning approaches to tackle this important challenge. Authors: Catherine Tong (University of Oxford); Christian A Schroeder de Witt (University of Oxford); Valentina Zantedeschi (GE Global Research); Daniele De Martini (University of Oxford); Alfredo Kalaitzis (University of Oxford); Matthew Chantry (University of Oxford); Duncan Watson-Parris (University of Oxford); Piotr Bilinski (University of Warsaw / University of Oxford)	Climate Science & Modeling
NeurIPS 2020	Machine learning for advanced solar cell production: adversarial denoising, sub-pixel alignment and the digital twin (Papers Track) Abstract and authors: (click to expand) Abstract: Photovoltaic is a main pillar to achieve the transition towards a renewable energy supply. In order to continue the tremendous cost decrease of the last decades, novel cell techologies and production processes are implemented into mass production to improve cell efficiency. Raising their full potential requires novel techniques of quality assurance and data analysis. We present three use-cases along the value chain where machine learning techniques are investigated for quality inspection and process optimization: Adversarial learning to denoise wafer images, alignment of surface structuring processes via sub-pixel coordinate regression, and the development of a digital twin for wafers and solar cells for material and process analysis. Authors: Matthias Demant (Fraunhofer ISE); Leslie Kurumundayil (Fraunhofer ISE); Philipp Kunze (Fraunhofer ISE); Aditya Kovvali (Fraunhofer ISE); Alexandra Woernhoer (Fraunhofer ISE); Stefan Rein (Fraunhofer ISE)	Power & Energy Heavy Industry and Manufacturing Computer Vision & Remote Sensing Unsupervised & Semi-Supervised Learning
NeurIPS 2020	Climate Change Driven Crop Yield Failures (Papers Track) Abstract and authors: (click to expand) Abstract: The effect of extreme temperatures, precipitation and variations in other meteorological factors affect crop yields, and hence climate change jeopardizes the entire food supply chain and dependent economic activities. We utilize Deep Neural Networks and Gaussian Processes for understanding crop yields as functions of climatological variables, and use change detection techniques to identify climatological thresholds where yield drops significantly. Authors: Somya Sharma (U. Minnesota); Deepak Ray (University of Minnesota); Snigdhansu Chatterjee (University of Minnesota)	Agriculture & Food
NeurIPS 2020	Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics (Papers Track) Abstract and authors: (click to expand) Abstract: We develop physics-constrained and control-oriented predictive deep learning models for the thermal dynamics of a real-world commercial office building. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our model mimics the structure of the building thermal dynamics model and leverages penalty methods to model inequality constraints. Additionally, we use constrained matrix parameterization based on the Perron-Frobenius theorem to bound the eigenvalues of the learned network weights. We interpret the stable eigenvalues as dissipativeness of the learned building thermal model. We demonstrate the effectiveness of the proposed approach on a dataset obtained from an office building with 20 thermal zones. Authors: Jan Drgona (Pacific Northwest National Laboratory); Aaron R Tuor (Pacific Northwest National Laboratory); Vikas Chandan (PNNL); Draguna Vrabie (PNNL)	Hybrid Physical Models Buildings
NeurIPS 2020	Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse (Papers Track) Abstract and authors: (click to expand) Abstract: People often turn to social media to comment upon and share information about major global events. Accordingly, social media is receiving increasing attention as a rich data source for understanding people's social, political and economic experiences of extreme weather events. In this paper, we contribute two novel methodologies that leverage Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people in May 2020. Authors: Ancil S Crayton (Booz Allen Hamilton); Joao Fonseca (NOVA Information Management School); Kanav Mehra (Independent Researcher); Jared Ross (Booz Allen Hamilton); Marcelo Sandoval-Castañeda (New York University Abu Dhabi); Michelle Ng (International Water Management Institute); Rachel von Gnechten (International Water Management Institute)	Disaster Management and Relief Natural Language Processing Unsupervised & Semi-Supervised Learning
NeurIPS 2020	FlowDB: A new large scale river flow, flash flood, and precipitation dataset (Papers Track) Abstract and authors: (click to expand) Abstract: Flooding results in 8 billion dollars of damage annually in the US and causes the most deaths of any weather related event. Due to climate change scientists expect more heavy precipitation events in the future. However, no current datasets exist that contain both hourly precipitation and river flow data. We introduce a novel hourly river flow and precipitation dataset and a second subset of flash flood events with damage estimates and injury counts. Using these datasets we create two challenges (1) general stream flow forecasting and (2) flash flood damage estimation. We also create a public benchmark and an Python package to enable easy adding of new models . Additionally, in the future we aim to augment our dataset with snow pack data and soil index moisture data to improve predictions Authors: Isaac Godfried (CoronaWhy)	Time-series Analysis Climate Science & Modeling Data Mining
NeurIPS 2020	Can Federated Learning Save The Planet ? (Papers Track) Abstract and authors: (click to expand) Abstract: Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL in particular is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection. \textit{However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL.} First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show FL, despite being slower to converge, can be a greener technology than data center GPUs. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency. Authors: Xinchi Qiu (University of Cambridge); Titouan Parcollet (University of Oxford); Daniel J Beutel (Adap GmbH / University of Cambridge); Taner Topal (Adap GmbH); Akhil Mathur (Nokia Bell Labs); Nicholas Lane (University of Cambridge and Samsung AI)	Power & Energy
NeurIPS 2020	FireSRnet: Geoscience-driven super-resolution of future fire risk from climate change (Papers Track) Abstract and authors: (click to expand) Abstract: With fires becoming increasingly frequent and severe across the globe in recent years, understanding climate change’s role in fire behavior is critical for quantifying current and future fire risk. However, global climate models typically simulate fire behavior at spatial scales too coarse for local risk assessments. Therefore, we propose a novel approach towards super-resolution (SR) enhancement of fire risk exposure maps that incorporates not only 2000 to 2020 monthly satellite observations of active fires but also local information on land cover and temperature. Inspired by SR architectures, we propose an efficient deep learning model trained for SR on fire risk exposure maps. We evaluate this model on resolution enhancement and find it outperforms standard image interpolation techniques at both 4x and 8x enhancement while having comparable performance at 2x enhancement. We then demonstrate the generalizability of this SR model over northern California and New South Wales, Australia. We conclude with a discussion and application of our proposed model to climate model simulations of fire risk in 2040 and 2100, illustrating the potential for SR enhancement of fire risk maps from the latest state-of-the-art climate models. Authors: Tristan C Ballard (Sust Global, Stanford University); Gopal Erinjippurath (Sust Global)	Computer Vision & Remote Sensing Climate Science & Modeling Societal Adaptation & Resilience
NeurIPS 2020	An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data (Papers Track) Abstract and authors: (click to expand) Abstract: While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries. Authors: Benjamin Rausch (Stanford); Kevin Mayer (Stanford); Marie-Louise Arlt (Stanford); Gunther Gust (University of Freiburg); Philipp Staudt (KIT); Christof Weinhardt (Karlsruhe Institute of Technology); Dirk Neumann (Universität Freiburg); Ram Rajagopal (Stanford University)	Power & Energy Buildings Computer Vision & Remote Sensing
NeurIPS 2020	Satellite imagery analysis for Land Use, Land Use Change and Forestry: A pilot study in Kigali, Rwanda (Papers Track) Abstract and authors: (click to expand) Abstract: Estimating greenhouse gases from the Agriculture, Forestry and Other land Use (AFOLU) sector is very challenging partly due to the unavailability of data (particularly for land use and land use change sectors) and inadequate experts to analyze this data in case it is available. We used Collect Earth together with Machine Learning techniques to be able to predict and classify all the land use types based on some few points collected using Collect Earth. We investigated the adoption of this tool and technology in Rwanda to help its national and sub-national inventories. The use of Collect Earth and the Machine Learning (ML) implementation will help Rwanda monitor and predict its Land Use, Land Use Change, and Forestry in a cost-effective manner whiles enhancing the quality of reports submitted to national and international bodies whiles introducing a new approach. Among the classification algorithms we tested, we had an overall classification accuracy of 97% using the Classification and Regression Trees (CART) algorithm to to predict the six land Use classes across the country. Authors: Bright Aboh (African Institute for Mathematical Sciences); Alphonse Mutabazi (UN Environment Program)	Forests Land Use Computer Vision & Remote Sensing Agriculture & Food
NeurIPS 2020	EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variables and Earth’s surface at such local scales remains unresolved by current physical models. Large Earth observation datasets now enable us to create machine learning models capable of translating coarse weather information into high-resolution Earth surface forecasts encompassing localized climate impacts. Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts. Video prediction has been tackled with deep learning models. Developing such models requires analysis-ready datasets. We introduce EarthNet2021, a new, curated dataset containing target spatio-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale (1.28 km) weather variables. With over 32000 samples it is suitable for training deep neural networks. Comparing multiple Earth surface forecasts is not trivial. Hence, we define the EarthNetScore, a novel ranking criterion for models forecasting Earth surface reflectance. For model intercomparison we frame EarthNet2021 as a challenge with four tracks based on different test sets. These allow evaluation of model validity and robustness as well as model applicability to extreme events and the complete annual vegetation cycle. In addition to forecasting directly observable weather impacts through satellite-derived vegetation indices, capable Earth surface models will enable downstream applications such as crop yield prediction, forest health assessments, coastline management, or biodiversity monitoring. Find data, code, and how to participate at www.earthnet.tech . Authors: Christian Requena-Mesa (Computer Vision Group, Friedrich Schiller University Jena; DLR Institute of Data Science, Jena; Max Planck Institute for Biogeochemistry, Jena); Vitus Benson (Max-Planck-Institute for Biogeochemistry); Jakob Runge (Institute of Data Science, German Aerospace Center (DLR)); Joachim Denzler (Computer Vision Group, Friedrich Schiller University Jena, Germany); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena)	Climate Science & Modeling Disaster Management and Relief Ecosystems & Biodiversity Computer Vision & Remote Sensing Data Mining Generative Modeling Hybrid Physical Models Agriculture & Food Forests
NeurIPS 2020	DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet (Papers Track) Abstract and authors: (click to expand) Abstract: Accurate waste disposal, at the point of disposal, is crucial to fighting climate change. When materials that could be recycled or composted get diverted into landfills, they cause the emission of potent greenhouse gases such as methane. Current attempts to reduce erroneous waste disposal are expensive, inaccurate, and confusing. In this work, we propose DeepWaste, an easy-to-use mobile app, that utilizes highly optimized deep learning techniques to provide users instantaneous waste classification into trash, recycling, and compost. We experiment with several convolution neural network architectures to detect and classify waste items. Our best model, a deep learning residual neural network with 50 layers, achieves an average precision of 0.881 on the test set. We demonstrate the performance and efficiency of our app on a set of real-world images. Authors: Yash Narayan (The Nueva School)
NeurIPS 2020	Machine Learning Climate Model Dynamics: Offline versus Online Performance (Papers Track) Abstract and authors: (click to expand) Abstract: Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly resolve processes larger than 100 km and approximate any process occurring below this scale (e.g. thunderstorms) using so-called parametrizations. Machine learning could improve upon the accuracy of some traditional physical parametrizations by learning from so-called global cloud-resolving models. We compare the performance of two machine learning models, random forests (RF) and neural networks (NNs), at parametrizing the aggregate effect of moist physics in a 3 km resolution global simulation with an atmospheric model. The NN outperforms the RF when evaluated offline on a testing dataset. However, when the ML models are coupled to an atmospheric model run at 200 km resolution, the NN-assisted simulation crashes with 7 days, while the RF-assisted simulations remain stable. Both runs produce more accurate weather forecasts than a baseline configuration, but globally averaged climate variables drift over longer timescales. Authors: Noah D Brenowitz (Vulcan Inc.); Brian Henn (Vulcan, Inc.); Spencer Clark (Vulcan, Inc.); Anna Kwa (Vulcan, Inc.); Jeremy McGibbon (Vulcan, Inc.); W. Andre Perkins (Vulcan, Inc.); Oliver Watt-Meyer (Vulcan, Inc.); Christopher S. Bretherton (Vulcan, Inc.)	Climate Science & Modeling Hybrid Physical Models
NeurIPS 2020	OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: At least a quarter of the warming that the Earth is experiencing today is due to anthropogenic methane emissions. There are multiple satellites in orbit and planned for launch in the next few years which can detect and quantify these emissions; however, to attribute methane emissions to their sources on the ground, a comprehensive database of the locations and characteristics of emission sources worldwide is essential. In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions. We use the best algorithm, which we call OGNet, together with expert review to identify the locations of oil refineries and petroleum terminals in the U.S. We show that OGNet detects many facilities which are not present in four standard public datasets of oil and gas infrastructure. All detected facilities are associated with characteristics critical to quantifying and attributing methane emissions, including the types of infrastructure and number of storage tanks. The data curated and produced in this study is freely available at https://link/provided/in/camera/ready/version. Authors: Hao Sheng (Stanford University); Jeremy A Irvin (Stanford); Sasankh Munukutla (Stanford University); Shawn Zhang (Stanford University); Christopher Cross (Stanford University); Zutao Yang (Stanford University); Kyle Story (Descartes Labs); Rose Rustowicz (Descartes Labs); Cooper Elsworth (Descartes Labs); Mark Omara (Environmental Defense Fund); Ritesh Gautam (Environmental Defense Fund); Rob Jackson (Stanford University); Andrew Ng (Stanford University)	Power & Energy Computer Vision & Remote Sensing
NeurIPS 2020	VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder (Papers Track) Abstract and authors: (click to expand) Abstract: Remote sensing of Chlorophyll-a is vital in monitoring climate change. Chlorphylla measurements give us an idea of the algae concentrations in the ocean, which lets us monitor ocean health. However, a common problem is that the satellites used to gather the data are commonly obstructed by clouds and other artifacts. This means that time series data from satellites can suffer from spatial data loss. There are a number of algorithms that are able to reconstruct the missing parts of these images to varying degrees of accuracy, with Data INterpolating Empirical Orthogonal Functions (DINEOF) being the current standard. However, DINEOF is slow, suffers from accuracy loss in temporally homogenous waters, reliant on temporal data, and only able to generate a single potential reconstruction. We propose a machine learning approach to reconstruction of Chlorophyll-a data using a Variational Autoencoder (VAE). Our accuracy results to date are competitive with but slightly less accurate than DINEOF. We show the benefits of our method including vastly decreased computation time and ability to generate multiple potential reconstructions. Lastly, we outline our planned improvements and future work. Authors: Matthew Ehrler (University of Victoria); Neil Ernst (University of Victoria)	Computer Vision & Remote Sensing Climate Science & Modeling Ecosystems & Biodiversity Generative Modeling
NeurIPS 2020	A Comparison of Data-Driven Models for Predicting Stream Water Temperature (Papers Track) Abstract and authors: (click to expand) Abstract: Changes to the Earth's climate are expected to negatively impact water resources in the future. It is important to have accurate modelling of river flow and water quality to make optimal decisions for water management. Machine learning and deep learning models have become promising methods for making such hydrological predictions. Using these models, however, requires careful consideration both of data constraints and of model complexity for a given problem. Here, we use machine learning (ML) models to predict monthly stream water temperature records at three monitoring locations in the Northwestern United States with long-term datasets, using meteorological data as predictors. We fit three ML models: a Multiple Linear Regression, a Random Forest Regression, and a Support Vector Regression, and compare them against two baseline models: a persistence model and historical model. We show that all three ML models are reasonably able to predict mean monthly stream temperatures with root mean-squared errors (RMSE) ranging from 0.63-0.91 degrees Celsius. Of the three ML models, Support Vector Regression performs the best with an error of 0.63-0.75 degrees Celsius. However, all models perform poorly on extreme values of water temperature. We identify the need for machine learning approaches for predicting extreme values for variables such as water temperature, since it has significant implications for stream ecosystems and biota. Authors: Helen Weierbach (Lawrence Berkeley); Aranildo Lima (Aquatic Informatics); Danielle Christianson (Lawrence Berkeley National Lab); Boris Faybishenko (Lawrence Berkeley National Lab); Val Hendrix (Lawrence Berkeley National Lab); Charuleka Varadharajan (Lawrence Berkeley National Lab)	Ecosystems & Biodiversity Climate Science & Modeling Time-series Analysis
NeurIPS 2020	Automated Salmonid Counting in Sonar Data (Papers Track) Abstract and authors: (click to expand) Abstract: The prosperity of salmonids is crucial for several ecological and economic functions. Accurately counting spawning salmonids during their seasonal migration is essential in monitoring threatened populations, assessing the efficacy of recovery strategies, guiding fishing season regulations, and supporting the management of commercial and recreational fisheries. While several different methods exist for counting river fish, they all rely heavily on human involvement, introducing a hefty financial and time burden. In this paper we present an automated fish counting method that utilizes data captured from ARIS sonar cameras to detect and track salmonids migrating in rivers. Our results show that our fully automated system has a 19.3% per-clip error when compared to human counting performance. There is room to improve, but our system can already decrease the amount of time field biologists and fishery managers need to spend manually watching ARIS clips. Authors: Peter Kulits (Caltech); Angelina Pan (Caltech); Sara M Beery (Caltech); Erik Young (Trout Unlimited); Pietro Perona (California Institute of Technology); Grant Van Horn (Cornell University)	Ecosystems & Biodiversity Climate Science & Modeling Heavy Industry and Manufacturing Computer Vision & Remote Sensing Time-series Analysis
NeurIPS 2020	Short-term PV output prediction using convolutional neural network: learning from an imbalanced sky images dataset via sampling and data augmentation (Papers Track) Abstract and authors: (click to expand) Abstract: Integrating photovoltaics (PV) into electricity grids is one of the major pathways towards a low-carbon energy system. However, the biggest challenge is the strong fluctuation in PV power generation. In recent years, sky-image-based PV output prediction using deep neural networks has emerged as a promising approach to alleviate the uncertainty. Despite the research surge in exploring different model architectures, there is currently no study addressing the issue of learning from an imbalanced sky images dataset, the outcome of which would be highly beneficial for improving the reliability of existing and new solar forecasting models. In this study, we train convolutional neural network (CNN) models from an imbalanced sky images dataset for two disparate PV output prediction tasks, i.e., nowcast and forecast. We empirically examine the efficacy of using different sampling and data augmentation approaches to create synthesized dataset for model development. We further apply a three-stage selection approach to determine the optimal sampling approach, data augmentation technique and oversampling rate. Authors: Yuhao Nie (Stanford University); Ahmed S Zamzam (The National Renewable Energy Laboratory); Adam Brandt (Stanford University)	Computer Vision & Remote Sensing Climate Science & Modeling
NeurIPS 2020	OfficeLearn: An OpenAI Gym Environment for Building Level Energy Demand Response (Papers Track) Abstract and authors: (click to expand) Abstract: Energy Demand Response (DR) will play a crucial role in balancing renewable energy generation with demand as grids decarbonize. There is growing interest in developing Reinforcement Learning (RL) techniques to optimize DR pricing, as pricing set by electric utilities often cannot take behavioral irrationality into account. However, so far, attempts to standardize RL efforts in this area do not exist. In this paper, we present a first of the kind OpenAI gym environment for testing DR with occupant level building dynamics. We demonstrate the variety of parameters built into our office environment allowing the researcher to customize a building to meet their specifications of interest. We hope that this work enables future work in DR in buildings. Authors: Lucas Spangher (U.C. Berkeley); Akash Gokul (University of California at Berkeley); Utkarsha Agwan (U.C. Berkeley); Joseph Palakapilly (UC Berkeley); Manan Khattar (University of California at Berkeley); Akaash Tawade (University of California at Berkeley); Costas J. Spanos (University of California at Berkeley)	Buildings Power & Energy Reinforcement Learning Time-series Analysis
NeurIPS 2020	Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya (Papers Track) Abstract and authors: (click to expand) Abstract: Glacier mapping is key to ecological monitoring in the Hindu Kush Himalaya region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and correct model predictions, with the ultimate aim of accelerating the glacier mapping process. Authors: Shimaa Baraka (Mila); Benjamin Akera (Makerere University); Bibek Aryal (The University of Texas at El Paso); Tenzing Sherpa (International Centre for Integrated Mountain Development); Finu Shrestha (International Centre for Integrated Mountain Development); Anthony Ortiz (Microsoft); Kris Sankaran (University of Wisconsin-Madison); Juan M Lavista Ferres (Microsoft); Mir A Matin (International Center for Integrated Mountain Development); Yoshua Bengio (Mila)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2020	Data-driven modeling of cooling demand in a commercial building (Papers Track) Abstract and authors: (click to expand) Abstract: Heating, ventilation, and air conditioning (HVAC) systems account for 30% of the total energy consumption in buildings. Design and implementation of energy-efficient schemes can play a pivotal role in minimizing energy usage. As an important first step towards improved HVAC system controls, this study proposes a new framework for modeling the thermal response of buildings by leveraging data measurements and formulating a data-driven system identification model. The proposed method combines principal component analysis (PCA) to identify the most significant predictors that influence the cooling demand of a building with an auto-regressive integrated moving average with exogenous variables (ARIMAX) model. The performance of the developed model was evaluated both analytically and visually. It was found that our PCA-based ARIMAX (2-0-5) model was able to accurately forecast the cooling demand for the prediction horizon of 7 days. In this work, the actual measurements from a university campus building are used for model development and validation. Authors: Aqsa Naeem (Stanford University); Sally Benson (Stanford University); Jacques de Chalendar (Stanford University)	Buildings Power & Energy Hybrid Physical Models
NeurIPS 2020	Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR (Papers Track) Abstract and authors: (click to expand) Abstract: In an effort to provide optimal inputs to downstream modeling systems (e.g., a hydrodynamics model that simulates the water circulation of a lake), we hereby strive to enhance resolution of precipitation fields from a weather model by up to 9x. We test two super-resolution models: the enhanced super-resolution generative adversarial networks (ESRGAN) proposed in 2017, and the content adaptive resampler (CAR) proposed in 2020. Both models outperform simple bicubic interpolation, with the ESRGAN exceeding expectations for accuracy. We make several proposals for extending the work to ensure it can be useful tool for quantifying the impact of climate change on local ecosystems while removing reliance on energy-intensive, high-resolution weather model simulations. Authors: Campbell Watson (IBM); Chulin Wang (Northwestern University); Tim Lynar (University of New South Wales); Komminist Weldemariam (IBM Research)	Ecosystems & Biodiversity
NeurIPS 2020	Emerging Trends of Sustainability Reporting in the ICT Industry: Insights from Discriminative Topic Mining (Papers Track) Abstract and authors: (click to expand) Abstract: The Information and Communication Technologies (ICT) industry has a considerable climate change impact and accounts for approximately 3 percent of global carbon emissions. Despite the increasing availability of sustainability reports provided by ICT companies, we still lack a systematic understanding of what has been disclosed at an industry level. In this paper, we make the first major effort to use modern unsupervised learning methods to investigate the sustainability reporting themes and trends of the ICT industry over the past two decades. We build a cross-sector dataset containing 22,534 environmental reports from 1999 to 2019, of which 2,187 are ICT specific. We then apply CatE, a text embedding based topic modeling method, to mine specific keywords that ICT companies use to report on climate change and energy. As a result, we identify (1) important shifts in ICT companies' climate change narratives from physical metrics towards climate-related disasters, (2) key organizations with large influence on ICT companies, and (3) ICT companies' increasing focus on data center and server energy efficiency. Authors: Lin Shi (Stanford University); Nhi Truong Vu (Stanford University)	Heavy Industry and Manufacturing Climate Policy Data Mining Natural Language Processing Unsupervised & Semi-Supervised Learning
NeurIPS 2020	Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models can only take us so far, as they require significant computational resources, especially when attempting to characterize extreme events, which are rare and thus demand long and numerous simulations in order to accurately represent their changing statistics. Here we use deep learning in a proof of concept that lays the foundation for emulating global climate model output for different scenarios. We train two "loosely conditioned" Generative Adversarial Networks (GANs) that emulate daily precipitation output from a fully coupled Earth system model: one GAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are trained to produce spatiotemporal samples: 32 days of precipitation over a 64x128 regular grid discretizing the globe. We evaluate the generator with a set of related performance metrics based upon KL divergence, and find the generated samples to be nearly as well matched to the test data as the validation data is to test. We also find the generated samples to accurately estimate the mean number of dry days and mean longest dry spell in the 32 day samples. Our trained GANs can rapidly generate numerous realizations at a vastly reduced computational expense, compared to large ensembles of climate models, which greatly aids in estimating the statistics of extreme events. Authors: Alex Ayala (Western Washington University); Chris Drazic (Western Washington University); Brian Hutchinson (Western Washington University); Ben Kravitz (Indiana University); Claudia Tebaldi (Joint Global Change Research Institute)	Generative Modeling Climate Science & Modeling
NeurIPS 2020	The Peruvian Amazon Forestry Dataset: A Leaf Image Classification Corpus (Papers Track) Abstract and authors: (click to expand) Abstract: This paper introduces the Peruvian Amazon Forestry Dataset, which includes 59,441 leaves samples from ten of the most profitable and endangered Amazon timber-tree species. Besides, the proposal includes a background removal algorithm to feed a fine-tuned CNN. We evaluate the quantitative (accuracy metric) and qualitative (visual interpretation) impacts of each stage by ablation experiments. The results show a 96.64 % training accuracy and 96.52 % testing accuracy on the VGG-19 model. Furthermore, the visual interpretation of the model evidences that leaf venations have the highest correlation in the plant recognition task. Authors: Gerson Waldyr Vizcarra Aguilar (San Pablo Catholic University); Danitza Bermejo (Universidad Nacional del Altiplano); Manasses A. Mauricio (Universidad Católica San Pablo); Ricardo Zarate (Instituto de Investigaciones de la Amazonía Peruana); Erwin Dianderas (Instituto de Investigaciones de la Amazonía Peruana)	Meta- and Transfer Learning Forests
NeurIPS 2020	Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding the changing distributions of butterflies gives insight into the impacts of climate change across ecosystems and is a prerequisite for conservation efforts. eButterfly is a citizen science website created to allow people to track the butterfly species around them and use these observations to contribute to research. However, correctly identifying butterfly species is a challenging task for non-specialists and currently requires the involvement of entomologists to verify the labels of novice users on the website. We have developed a computer vision model to label butterfly images from eButterfly automatically, decreasing the need for human experts. We employ a model that incorporates geographic and temporal information of where and when the image was taken, in addition to the image itself. We show that we can successfully apply this spatiotemporal model for fine-grained image recognition, significantly improving the accuracy of our classification model compared to a baseline image recognition system trained on the same dataset. Authors: Marta Skreta (University of Toronto); Sasha Luccioni (Mila); David Rolnick (McGill University, Mila)	Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2020	High-resolution global irrigation prediction with Sentinel-2 30m data (Papers Track) Abstract and authors: (click to expand) Abstract: An accurate and precise understanding of global irrigation usage is crucial for a variety of climate science efforts. Irrigation is highly energy-intensive, and as population growth continues at its current pace, increases in crop need and water usage will have an impact on climate change. Precise irrigation data can help with monitoring water usage and optimizing agricultural yield, particularly in developing countries. Irrigation data, in tandem with precipitation data, can be used to predict water budgets as well as climate and weather modeling. With our research, we produce an irrigation prediction model that combines unsupervised clustering of Normalized Difference Vegetation Index (NDVI) temporal signatures with a precipitation heuristic to label the months that irrigation peaks for each cropland cluster in a given year. We have developed a novel irrigation model and Python package ("Irrigation30") to generate 30m resolution irrigation predictions of cropland worldwide. With a small crowdsourced test set of cropland coordinates and irrigation labels, using a fraction of the resources used by the state-of-the-art NASA-funded GFSAD30 project with irrigation data limited to India and Australia, our model was able to achieve consistency scores in excess of 97% and an accuracy of 92% in a small geo-diverse randomly sampled test set. Authors: Will Hawkins (UC Berkeley); Weixin Wu (UC Berkeley); Sonal Thakkar (UC Berkeley); Puya Vahabi (UC Berkeley); Alberto Todeschini (UC Berkeley)	Unsupervised & Semi-Supervised Learning Agriculture & Food
NeurIPS 2020	Do Occupants in a Building exhibit patterns in Energy Consumption? Analyzing Clusters in Energy Social Games (Papers Track) Abstract and authors: (click to expand) Abstract: Energy use in buildings account for approximately half of global electricity consumption and a significant amount of CO2 emissions. To encourage energy efficient behavior among occupants in a building, energy social games have emerged to be a successful strategy leveraging human-in-the-loop strategy and engaging users in a competitive game with incentives for energy efficient behavior. Prior works involve an incentive design mechanism which is dependent on knowledge of utility functions (energy use behavior) for the users, which is hard to compute when the number of users is high, common in buildings. We propose that the utilities can be grouped to a relatively small number of clusters, which can then be targeted with tailored incentives. Proposed work performs the above segmentation by learning the features leading to human decision making towards energy usage in competitive environment. We propose a graphical lasso based approach with explainable nature for such segmentation, by studying the feature correlations in a real-world energy social game dataset. Authors: Hari Prasanna Das (UC Berkeley); Ioannis C. Konstantakopoulos (UC Berkeley); Aummul Baneen Manasawala (UC Berkeley); Tanya Veeravalli (UC Berkeley); Huihan Liu (UC Berkeley); Costas J. Spanos (University of California at Berkeley)	Buildings
NeurIPS 2020	In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness (Papers Track) Abstract and authors: (click to expand) Abstract: Many machine learning applications used to tackle climate change involve lots of unlabeled data (such as satellite imagery) along with auxiliary information such as climate data. In this work, we show how to use auxiliary information in a semi-supervised setting to improve both in-distribution and out-of-distribution (OOD) accuracies (e.g. for countries in Africa where we have very little labeled data). We show that 1) on real-world datasets, the common practice of using auxiliary information as additional input features improves in-distribution error but can hurt OOD. Oppositely, we find that 2) using auxiliary information as outputs of auxiliary tasks to pre-train a model improves OOD error. 3) To get the best of both worlds, we introduce In-N-Out, which first trains a model with auxiliary inputs and uses it to pseudolabel all the in-distribution inputs, then pre-trains a model on OOD auxiliary outputs and fine-tunes this model with the pseudolabels (self-training). We show both theoretically and empirically on remote sensing datasets for land cover prediction and cropland prediction that In-N-Out outperforms auxiliary inputs or outputs alone on both in-distribution and OOD error. Authors: Robbie M Jones (Stanford University); Sang Michael Xie (Stanford University); Ananya Kumar (Stanford University); Fereshte Khani (Stanford); Tengyu Ma (Stanford University); Percy Liang (Stanford University)	Meta- and Transfer Learning Computer Vision & Remote Sensing Unsupervised & Semi-Supervised Learning
NeurIPS 2020	Climate-FEVER: A Dataset for Verification of Real-World Climate Claims (Papers Track) Abstract and authors: (click to expand) Abstract: Our goal is to introduce \textsc{climate-fever}, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to help and encourage work on improving algorithms for retrieving climate-specific information and detecting fake news in social and mass media to reduce the impact of misinformation on the formation of public opinion on climate change. We adapt the methodology of \textsc{fever} \cite{thorne2018fever}, the largest dataset of artificially designed claims, to real-life claims collected from the Internet. Although during this process, we could count on the support of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the \textsc{fever} framework, which provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of an exciting long-term joint effort by the climate science and \textsc{ai} community to develop robust algorithms to verify the facts for climate-related claims. Authors: Markus Leippold (University of Zurich); Thomas Diggelmann (ETH Zurich)	Natural Language Processing Behavioral and Social Science Climate Finance & Economics
NeurIPS 2020	Understanding global fire regimes using Artificial Intelligence (Papers Track) Abstract and authors: (click to expand) Abstract: Improved understanding of fire activity and its influencing factors will impact the way we interact and coexist with not only the fire itself but also with the ecosystem as a whole. We consolidate more than 20 million wildfire records between 2000 and 2018 across the six continents. This data is processed with artificial intelligence methods to discover global fire regimes, areas with characteristic fire behavior over long periods. We discover 15 groups with clear differences in fire-related historical behavior. Despite sharing historical fire behavior, regions belonging to the same group present significant differences in location and influencing factors. Groups are further divided into 62 regimes based on spatial aggregation patterns, providing a comprehensive characterization. This allows an interpretation of how a combination of vegetation, climate, and demographic features results in a specific fire regime. The current work expands on existing classification efforts and is a step forward in addressing the complex challenge of characterizing global fire regimes. Authors: Cristobal Pais (University of California Berkeley); Jose-Ramon Gonzalez (CTFC); Pelagy Moudio (University of California Berkeley); Jordi Garcia-Gonzalo (CTFC); Marta C. González (Berkeley); Zuo-Jun Shen (University of California, Berkeley)	Climate Science & Modeling Disaster Management and Relief
NeurIPS 2020	ClimaText: A Dataset for Climate Change Topic Detection (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT~\cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic. Authors: Markus Leippold (University of Zurich); Francesco Saverio Varini (ETH)	Natural Language Processing Behavioral and Social Science
NeurIPS 2020	Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Under the effects of global warming, extreme events such as floods and droughts are increasing in frequency and intensity. This trend directly affects communities and make all the more urgent widening the access to accurate precipitation forecasting systems for disaster preparedness. Nowadays, weather forecasting relies on numerical models necessitating massive computing resources that most developing countries cannot afford. Machine learning approaches are still in their infancy but already show the promise for democratizing weather predictions, by leveraging any data source and requiring less compute. In this work, we propose a methodology for data-driven and physics-aware global precipitation forecasting from satellite imagery. To fully take advantage of the available data, we design the system as three elements: 1. The atmospheric state is estimated from recent satellite data. 2. The atmospheric state is propagated forward in time. 3. The atmospheric state is used to derive the precipitation intensity within a nearby time interval. In particular, our use of stochastic methods for forecasting the atmospheric state represents a novel application in this domain. Authors: Valentina Zantedeschi (GE Global Research); Daniele De Martini (University of Oxford); Catherine Tong (University of Oxford); Christian A Schroeder de Witt (University of Oxford); Piotr Bilinski (University of Warsaw / University of Oxford); Alfredo Kalaitzis (University of Oxford); Matthew Chantry (University of Oxford); Duncan Watson-Parris (University of Oxford)	Climate Science & Modeling Disaster Management and Relief Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2020	A Generative Adversarial Gated Recurrent Network for Power Disaggregation & Consumption Awareness (Papers Track) Abstract and authors: (click to expand) Abstract: Separating the household aggregated power signal into its additive sub-components is called energy (power) disaggregation or Non-Intrusive Load Monitoring. NILM can play an instrumental role as a driver towards consumer energy consumption awareness and behavioral change. In this paper, we propose EnerGAN++, a model based on GANs for robust energy disaggregation. We propose a unified autoencoder (AE) and GAN architecture, in which the AE achieves a non-linear power signal source separation. The discriminator performs sequence classification, using a recurrent CNN to handle the temporal dynamics of an appliance energy consumption time series. Experimental results indicate the proposed method’s superiority compared to the current state of the art. Authors: Maria Kaselimi (National Technical University of Athens); Athanasios Voulodimos (University of West Attica); Nikolaos Doulamis (National Technical University of Athens); Anastasios Doulamis (Technical University of Crete); Eftychios Protopapadakis (National Technical University of Athens)	Power & Energy Generative Modeling
NeurIPS 2020	Deep Fire Topology: Understanding the role of landscape spatial patterns in wildfire susceptibility (Papers Track) Abstract and authors: (click to expand) Abstract: Increasing wildfire activity across the globe has become an urgent issue with enormous ecological and social impacts. While there is evidence that landscape topology affects fire growth, no study has yet reported its potential influence on fire ignition. This study proposes a deep learning framework focused on understanding the impact of different landscape topologies on the ignition of a wildfire and the rationale behind these results. Our model achieves an accuracy of above 90\% in fire occurrence prediction, detection, and classification of risky areas by only exploiting topological pattern information from 17,579 landscapes. This study reveals the strong potential of landscape topology in wildfire occurrence prediction and its implications for similar research. The proposed methodology can be applied to multiple fields/studies to understand and capture the role and impact of different topological features and their interactions. Authors: Cristobal Pais (University of California Berkeley); Alejandro Miranda (University of Chile); Jaime Carrasco (University of Chile); Zuo-Jun Shen (University of California, Berkeley)	Computer Vision & Remote Sensing Disaster Management and Relief Forests
NeurIPS 2020	Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Natural disasters ravage the world's cities, valleys, and shores on a monthly basis. Having precise and efficient mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we train multiple convolutional neural networks to assess building damage on a per-building basis. In order to investigate how to best classify building damage, we present a highly interpretable deep-learning methodology that seeks to explicitly convey the most useful information required to train an accurate classification model. We also delve into which loss functions best optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal loss function to use and that including the type of disaster that caused the damage in combination with a pre- and post-disaster image best predicts the level of damage caused. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by climate change. Authors: Thomas Y Chen (The Academy for Mathematics, Science, and Engineering)	Computer Vision & Remote Sensing Buildings Interpretable ML
NeurIPS 2020	Long-Range Seasonal Forecasting of 2m-Temperature with Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: A significant challenge in seasonal climate prediction is whether a prediction can beat climatology. We hereby present results from two data-driven models - a convolutional (CNN) and a recurrent (RNN) neural network - that predict 2m temperature out to 52 weeks for six geographically-diverse locations. The motivation for testing the two classes of ML models is to allow the CNN to leverage information related to teleconnections and the RNN to leverage long-term historical temporal signals. The ML models boast improved accuracy of long-range temperature forecasts up to a lead time of 30 weeks for PCC and up 52 weeks for RMSESS, however only for select locations. Further iteration is required to ensure the ML models have value beyond regions where the climatology has a noticeably reduced correlation skill, namely the tropics. Authors: Etienne E Vos (IBM); Ashley Gritzman (IBM); Sibusisiwe Makhanya (IBM Research); Thabang Mashinini (IBM); Campbell Watson (IBM)	Climate Science & Modeling
NeurIPS 2020	Explaining Complex Energy Systems: A Challenge (Proposals Track) Abstract and authors: (click to expand) Abstract: Designing future low-carbon, sector-coupled energy systems is a complex task. The work is therefore often supported by software tools that model and optimize possible energy systems. These tools typically have high dimensional inputs and outputs and are tailored towards domain experts. The final investment decisions to implement a certain system, however, are mostly made by people with little time and prior knowledge, thus unable to understand models and their input data used in these tools. Since such decisions are often connected to significant personal consequences for the decision makers, it is not enough for them to rely on experts only. They need an own, at least rough understanding. Explaining the key rationales behind complex energy system designs to non-expert decision makers in a short amount of time is thus a critical task for realizing projects of the energy transition in practice. It is also an interesting, novel challenge for the explainable AI community. Authors: Jonas Hülsmann (TU Darmstadt); Florian Steinke (TU Darmstadt)	Interpretable ML Climate Finance & Economics Power & Energy
NeurIPS 2020	The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Machine learning has the potential to aid in mitigating the human effects of climate change. Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it. For these methods to be the most effective they must consider relevant social-psychological factors for each individual. Of social-psychological factors at play in climate change, affect has been previously identified as a key element in perceptions and willingness to engage in mitigative behaviours. In this work, we propose an investigation into how affect could be incorporated to enhance machine learning based interventions for climate change. We propose using affective agent-based modelling for climate change as well as the use of a simulated climate change social dilemma to explore the potential benefits of affective machine learning interventions. Behavioural and informational interventions can be a powerful tool in helping humans adopt mitigative behaviours. We expect that utilizing affective ML can make interventions an even more powerful tool and help mitigative behaviours become widely adopted. Authors: Kyle Tilbury (University of Waterloo); Jesse Hoey (University of Waterloo)	Behavioral and Social Science Societal Adaptation & Resilience Causal & Bayesian Methods
NeurIPS 2020	A Way Toward Low-Carbon Shipping: Improving Port Operations Planning using Machine Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Despite being the most carbon-efficient way of transportation, shipping is an important contributor to air pollution especially in coastal areas. The sector’s impact on the environment still need mitigation, through different measures undertaken so far. Operational optimization of ports and ships is a step in shipping progress towards reducing the pollution. The main purpose of this research is to reduce the degree of error and uncertainty of some operational parameters using Machine Learning models, and provide port managers with accurate information to assist them in their decision-making process. Therefore, they will be able to manage ships speed and port times for a better monitoring of ships emissions during sea voyage and port stay. Authors: Sara El Mekkaoui (EMI Engineering School); Loubna Benabou (UQAR); Abdelaziz Berrado (EMI Engineering School)	Transportation
NeurIPS 2020	Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters (Proposals Track) Abstract and authors: (click to expand) Abstract: In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer’s household such as occupancy, habits and individual appliance usage. Yet smart metering infrastructure has the potential to vastly reduce carbon emissions from the energy sector through improved operating efficiencies. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at various scales to make load prediction possible whilst retaining the privacy of consumers’ raw energy consumption data. Authors: Christopher Briggs (Keele University); Zhong Fan (Keele University); Peter Andras (Keele University, School of Computing and Mathematics, Newcastle-under-Lyme, UK)	Power & Energy Buildings Societal Adaptation & Resilience Time-series Analysis
NeurIPS 2020	Leveraging Machine learning for Sustainable and Self-sufficient Energy Communities (Proposals Track) Abstract and authors: (click to expand) Abstract: Community Energies (CEs) are the next-generation energy management techniques that empowers citizens to interact with the energy market as self-consumers or prosumers actively. Successful implementation of CEs will promote sustainable energy production and consumption practices; thus, contributing to affordable and clean energy (SDG7) and climate action (SDG 13). Despite the potential of CEs, managing the overall power production and demand is challenging. This is because power is generated, distributed and controlled by several producers, each of which with different, and potentially conflicting, objectives. Thus, this project will investigate the role of machine learning approaches in smartening CEs, increasing energy awareness and enabling distributed energy resources planning and management. The project implementation will be centered around proof of concept development and capacity development in Africa. Authors: Anthony Faustine (University College Dublin); Lucas Pereira (ITI, LARSyS, Técnico Lisboa); Loubna Benabou (UQAR); Daniel Ngondya (The University of Dodoma)	Power & Energy
NeurIPS 2020	Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes. Authors: Irwin H McNeely (Carnegie Mellon University); Kimberly Wood (Mississippi State University); Niccolo Dalmasso (Carnegie Mellon University); Ann Lee (Carnegie Mellon University)	Disaster Management and Relief Interpretable ML
NeurIPS 2020	Hyperspectral Remote Sensing of Aquatic Microbes to Support Water Resource Management (Proposals Track) Abstract and authors: (click to expand) Abstract: Harmful algal blooms in drinking water supply and at recreational sites endanger human health. Excessive algal growth can result in low oxygen environments, making them uninhabitable for fish and other aquatic life. Harmful algae and algal blooms are predicted to increase in frequency and extent due to the warming climate, but microbial dynamics remain difficult to predict. Existing satellite remote sensing monitoring technologies are ill-equipped to discriminate harmful algae, while models do not adequately capture the complex controls on algal populations. This proposal explores the potential for Bayesian neural networks to detect phytoplankton pigments from hyperspectral remote sensing reflectance retrievals. Once developed, such a model could enable hyperspectral remote sensing retrievals to support decision making in water resource management as more advanced ocean color satellites are launched in the coming decade. While uncertainty quantification motivates the proposed use of Bayesian models, the interpretation of these uncertainties in an operational context must be carefully considered. Authors: Grace E Kim (Booz Allen Hamilton); Evan Poworoznek (NASA GSFC); Susanne Craig (NASA GSFC)	Ecosystems & Biodiversity Climate Science & Modeling
NeurIPS 2020	Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management (Proposals Track) Abstract and authors: (click to expand) Abstract: Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke. Here we show how machine learning, such as spectral clustering and manifold learning, can provide interpretable representations and powerful tools for differentiating between smoke types, hence providing forest managers with vital information on effective strategies to reduce climate-induced wildfires while minimizing production of harmful smoke. Authors: Lorenzo Tomaselli (Carnegie Mellon University); Coty Jen (Carnegie Mellon University); Ann Lee (Carnegie Mellon University)	Disaster Management and Relief Interpretable ML Unsupervised & Semi-Supervised Learning Forests
NeurIPS 2020	HECT: High-Dimensional Ensemble Consistency Testing for Climate Models (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate models play a crucial role in understanding the effect of environmental and man-made changes on climate to help mitigate climate risks and inform governmental decisions. Large global climate models such as the Community Earth System Model (CESM), developed by the National Center for Atmospheric Research, are very complex with millions of lines of code describing interactions of the atmosphere, land, oceans, and ice, among other components. As development of the CESM is constantly ongoing, simulation outputs need to be continuously controlled for quality. To be able to distinguish a ``climate-changing'' modification of the code base from a true climate-changing physical process or intervention, there needs to be a principled way of assessing statistical reproducibility that can handle both spatial and temporal high-dimensional simulation outputs. Our proposed work uses probabilistic classifiers like tree-based algorithms and deep neural networks to perform a statistically rigorous goodness-of-fit test of high-dimensional spatio-temporal data. Authors: Niccolo Dalmasso (Carnegie Mellon University); Galen Vincent (Carnegie Mellon University); Dorit Hammerling (Colorado School of Mines); Ann Lee (Carnegie Mellon University)	Climate Science & Modeling Uncertainty Quantification & Robustness
NeurIPS 2020	Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a proposed general purpose sensor-fusion semantic embedding model (Proposals Track) Abstract and authors: (click to expand) Abstract: Earth observation offers new insight into anthropogenic changes to nature, and how these changes are effecting (and are effected by) the built environment and the real economy. With the global availability of medium-resolution (10-30m) synthetic aperature radar (SAR) Sentinel-1 and multispectral Sentinel-2 imagery, machine learning can be employed to offer these insights at scale, unbiased to company- and country-level reporting. In this proposal, we document the development of an extensible corpus of labelled and unlabelled Sentinel-1 and Sentinel-2 imagery for the purposes of sensor fusion research. We make a large corpus and supporting code publicly available. We propose our own experiment design for the development of \emph{DeepSentinel}, a general-purpose semantic embedding model. Our aspiration is to provide pretrained models for transfer learning applications, significantly accelerating the impact of machine learning-enhanced earth observation on climate change mitigation. Authors: Lucas Kruitwagen (University of Oxford)	Computer Vision & Remote Sensing Land Use
NeurIPS 2020	Monitoring Shorelines via High-Resolution Satellite Imagery and Deep Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: Twentieth-century has seen an overall sea-level rise of 0.5m [7, 11] and the studies for the twenty-first-century project the overall increment within a range of 0.5m to 2m, considering high emission scenarios and rapid melting of major Antarctic glaciers. Naturally, this has a severe impact on a major percentage of the population inhabiting coastal land zones], with a recent study placing 110million people living below the local high tide line. Of all the different coastline types, sandy shores, forming 31% of the world’s beaches, undergo major erosion and accretion changes and hence are of special focus in this paper. Because of these reasons, it is paramount to regularly monitor the coastline changes across the world for better understanding and to create necessary preparation and mitigation strategies. Authors: Venkatesh Ramesh (HyperVerge); Digvijay Singh (HyperVerge)	Computer Vision & Remote Sensing
NeurIPS 2020	Graph Neural Networks for Improved El Niño Forecasting (Proposals Track) Abstract and authors: (click to expand) Abstract: Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheric patterns called teleconnections. Hence, we propose the application of spatiotemporal Graph Neural Networks (GNN) to forecast ENSO at long lead times, finer granularity and improved predictive skill than current state-of-the-art methods. The explicit modeling of information flow via edges may also allow for more interpretable forecasts. Preliminary results are promising and outperform state-of-the art systems for projections 1 and 3 months ahead. Authors: Salva Rühling Cachay (Technical University of Darmstadt); Emma Erickson (University of Illinois at Urbana-Champaign); Arthur F C Bucker (University of São Paulo); Ernest J Pokropek (Warsaw University of Techology); Willa Potosnak (Duquesne University); Salomey Osei (African Master's of Machine Intelligence(AMMI-GH)); Björn Lütjens (MIT)	Climate Science & Modeling Disaster Management and Relief Interpretable ML Time-series Analysis
NeurIPS 2020	Residue Density Segmentation for Monitoring and Optimizing Tillage Practices (Proposals Track) Abstract and authors: (click to expand) Abstract: "No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more complex, with the potential benefits of these approaches depending on numerous factors including a field's soil type(s), topography, and management history. Instead of using computer vision approaches to simply classify a field as till vs. no-till, we instead seek to identify the degree of residue coverage across a field through a probabilistic deep learning segmentation approach to enable more accurate analysis of carbon holding potential and realization. This approach will not only provide more precise insights into currently implemented practices, but also enable a more accurate identification process of fields with the greatest potential for adopting new practices to significantly impact carbon sequestration in agriculture. Authors: Jennifer Hobbs (IntelinAir); Ivan A Dozier (IntelinAir); Naira Hovakimyan (UIUC)	Carbon Capture & Sequestration Computer Vision & Remote Sensing Agriculture & Food
NeurIPS 2020	Machine Learning Informed Policy for Environmental Justice in Atlanta with Climate Justice Implications (Proposals Track) Abstract and authors: (click to expand) Abstract: Environmental hazards are not evenly distributed between the privileged and the protected classes in the U.S. Neighborhood zoning and planning of hazardous treatment, storage, and disposal facilities (TSDs) play a significant role in this sanctioned environmental racism. TSDs and toxic chemical releases into the air are accounted for by the U.S. Environmental Protection Agency’s (EPA) Toxic Release Inventories (TRIs) [2,4,7, 14]. TSDs and toxic chemical releases not only emit carbon dioxide and methane, which are the top two drivers of climate change, but also emit contaminants, such as arsenic, lead, and mercury into the water, air, and crops [12]. Studies on spatial disparities in TRIs and TSDs based on race/ethnicity and socioeconomic status (SES) in U.S. cities, such as Charleston, SC, San Joaquin Valley, CA, and West Oakland, CA showed that there are more TRIs and TSDs in non-white and low SES areas in those cities [2,4,7]. Environmental justice recognizes that the impacts of environmental burdens, such as socioeconomic and public health outcomes, are not equitably distributed, and in fact bear the heaviest burden on marginalized people, including communities of color and low-income communities [12]. In our case, environmental justice has a strong tie to climate justice since the TRIs release carbon dioxide and methane into the atmosphere. Authors: Lelia Hampton (Massachusetts Institute of Technology)	Climate Policy Behavioral and Social Science Climate Justice Unsupervised & Semi-Supervised Learning
NeurIPS 2020	A Multi-source, End-to-End Solution for Tracking Climate Change Adaptation in Agriculture (Proposals Track) Abstract and authors: (click to expand) Abstract: The impact of climate change on tropical agri-food systems will depend on both the direction and magnitude of climate change, and the agricultural sector’s adaptive capacity, the latter being affected by the chosen adaptation strategies. By extending SEIRS, a Satellite Remote Sensing (SRS) based system originally developed by the International Center for Tropical Agriculture - CIAT for monitoring U.S. Government-funded development programs across cropping areas in Africa, this research proposes the development and deployment of a scalable AI-based platform exploiting free-of-charge SRS data that will enable the agri-food sector to monitor a wide range of climate change adaptation (CCA) interventions in a timely, evidence-driven and comparable manner. The main contributions of the platform are i) ingesting and processing variety sources of SRS data with a considerable record (> 5 years) of vegetation greenness and precipitation (input data); ii) operating an end-to-end system by exploiting AI-based models suited to time series analysis such as Seq2Seq and Transformers; iii) providing customised proxies informing the success or failure of a given local CCA intervention(s). Authors: Alejandro Coca-Castro (Kings College London); Aaron Golden (NUI Galway); Louis Reymondin (The Alliance of Bioversity International and the International Center for Tropical Agriculture)	Computer Vision & Remote Sensing Climate Finance & Economics Climate Policy Time-series Analysis Agriculture & Food
NeurIPS 2020	Expert-in-the-loop Systems Towards Safety-critical Machine Learning Technology in Wildfire Intelligence (Proposals Track) Abstract and authors: (click to expand) Abstract: With the advent of climate change, wildfires are becoming more frequent and severe across several regions worldwide. To prevent and mitigate its effects, wildfire intelligence plays a pivotal role, e.g. to monitor the evolution of wildfires and for early detection in high-risk areas such as wildland-urban-interface regions. Recent works have proposed deep learning solutions for fire detection tasks, however the current limited databases prevent reliable real-world deployments. We propose the development of expert-in-the-loop systems that combine the benefits of semi-automated data annotation with relevant domain knowledge expertise. Through this approach we aim to improve the data curation process and contribute to the generation of large-scale image databases for relevant wildfire tasks and empower the application of machine learning techniques in wildfire intelligence in real scenarios. Authors: Maria João Sousa (IDMEC, Instituto Superior Técnico, Universidade de Lisboa); Alexandra Moutinho (IDMEC, Instituto Superior Técnico, Universidade de Lisboa); Miguel Almeida (ADAI, University of Coimbra)	Computer Vision & Remote Sensing Carbon Capture & Sequestration Disaster Management and Relief Interpretable ML Meta- and Transfer Learning Natural Language Processing Uncertainty Quantification & Robustness Unsupervised & Semi-Supervised Learning
NeurIPS 2020	ACED: Accelerated Computational Electrochemical systems Discovery (Proposals Track) Abstract and authors: (click to expand) Abstract: Large-scale electrification is vital to addressing the climate crisis, but many engineering challenges remain to fully electrifying both the chemical industry and transportation. In both of these areas, new electrochemical materials and systems will be critical, but developing these systems currently relies heavily on computationally expensive first-principles simulations as well as human-time-intensive experimental trial and error. We propose to develop an automated workflow that accelerates these computational steps by introducing both automated error handling in generating the first-principles training data as well as physics-informed machine learning surrogates to further reduce computational cost. It will also have the capacity to include automated experiments ``in the loop'' in order to dramatically accelerate the overall materials discovery pipeline. Authors: Rachel C Kurchin (CMU); Eric Muckley (Citrine Informatics); Lance Kavalsky (CMU); Vinay Hegde (Citrine Informatics); Dhairya Gandhi (Julia Computing); Xiaoyu Sun (CMU); Matthew Johnson (MIT); Alan Edelman (MIT); James Saal (Citrine Informatics); Christopher V Rackauckas (Massachusetts Institute of Technology); Bryce Meredig (Citrine Informatics); Viral Shah (Julia Computing); Venkat Viswanathan (Carnegie Mellon University)	Power & Energy Transportation Active Learning Hybrid Physical Models Interpretable ML Uncertainty Quantification & Robustness
NeurIPS 2020	Forecasting Marginal Emissions Factors in PJM (Proposals Track) Abstract and authors: (click to expand) Abstract: Many climate change applications rely on accurate forecasts of power grid emissions, but many forecasting methods can be expensive, sensitive to input errors, or lacking in domain knowledge. Motivated by initial experiments using deep learning and power system modeling techniques, we propose a method that combines the strengths of both of these approaches to forecast hourly day-ahead MEFs for the PJM region of the United States. Authors: Amy H Wang (Western University); Priya L Donti (Carnegie Mellon University)	Power & Energy Hybrid Physical Models Time-series Analysis
NeurIPS 2020	Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change (Proposals Track) Abstract and authors: (click to expand) Abstract: These changes will have a drastic impact on almost all forms of life in the ocean with further consequences on food security, ecosystem services in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to understand and quantify the consequences of these perturbations on the marine ecosystem. Understanding this phenomenon is not only an urgent but also a scientifically demanding task. Consequently, it is a problem that must be addressed with a scientific cohort approach, where multi-disciplinary teams collaborate to bring the best of different scientific areas. In this proposal paper, we describe our newly launched four-years project focused on developing new artificial intelligence, machine learning, and mathematical modeling tools to contribute to the understanding of the structure, functioning, and underlying mechanisms and dynamics of the global ocean symbiome and its relation with climate change. These actions should enable the understanding of our oceans and predict and mitigate the consequences of climate change. Authors: Nayat Sánchez Pi (Inria); Luis Martí (Inria); André Abreu (Fountation Tara Océans); Olivier Bernard (Inria); Colomban de Vargas (CNRS); Damien Eveillard (Univ. Nantes); Alejandro Maass (CMM, U. Chile); Pablo Marquet (PUC); Jacques Sainte-Marie (Inria); Julien Salomin (Inria); Marc Schoenauer (INRIA); Michele Sebag (LRI, CNRS, France)	Interpretable ML Carbon Capture & Sequestration Ecosystems & Biodiversity Active Learning Causal & Bayesian Methods Computer Vision & Remote Sensing Hybrid Physical Models Meta- and Transfer Learning Time-series Analysis
NeurIPS 2020	Machine Learning towards a Global Parameterisation of Atmospheric New Particle Formation and Growth (Proposals Track) Abstract and authors: (click to expand) Abstract: New particle formation (NPF) and growth in the atmosphere affects climate, weather, air quality, and human health. It is the first step of the complex process leading to cloud condensation nuclei (CCN) formation. Even though there is a wealth of observations from field measurements (in forests, high-altitude, polar regions, coastal and urban sites, aircraft campaigns), as well as laboratory studies of multi-component nucleation (including the CLOUD chamber at CERN), and improved nucleation theories, the NPF parameterisations in regional and global models are lacking. These deficiencies make the impacts of aerosols one of the highest sources of uncertainty in global climate change modelling, and associated impacts on weather and human health. We propose to use Machine Learning methods to overcome the challenges in modelling aerosol nucleation and growth, by ingesting the data from the multitude of available sources to create a single parameterisation applicable throughout the modelled atmosphere (troposphere and stratosphere at all latitudes) that efficiently encompasses all input ambient conditions and concentrations of relevant species. Authors: Theodoros Christoudias (Cyprus Institute); Mihalis A Nicolaou (Cyprus Institute)	Climate Science & Modeling
ICLR 2020	BISCUIT: Building Intelligent System Customer Investment Tools (Papers Track) Abstract and authors: (click to expand) Abstract: Energy use in buildings account for approximately half of global electricity consumption and a significant amount of CO2 emissions. Often, the higher energy usage in buildings are accounted to old and poorly maintained infrastructure and equipments. On the other hand, Smart buildings are capable of achieving energy efficiency by using intelligent services such as indoor positioning, personalized lighting, demand-based heating ventilation and air-conditioning, automatic fault detection and recovery etc. However, most buildings nowadays lack the basic components and infrastructure to support such services. The investment decision of intelligent system design and retrofit can be a daunting task, because it involves both hardware (sensors, actuators, servers) and software (operating systems, service algorithms), which have issues of compatibility, functionality constraints, and opportunities of co-design of synergy. Our work proposes a user-oriented investment decision toolset using optimization and machine learning techniques aimed at handling the complexity of exploration in the large design space and to enhance cost-effectiveness, energy efficiency, and human-centric values. The toolset is demonstrated in a case study to retrofit a medium-sized building, where it is shown to propose a design that significantly lowers the overall investment cost while achieving user specifications. Authors: Ming Jin (U.C. Berkeley); Ruoxi Jia (UC Berkeley); Hari Prasanna Das (UC Berkeley); Wei Feng (Lawrence Berkeley National Laboratory); Costas J. Spanos (University of California at Berkeley)	Buildings
ICLR 2020	Deep Reinforcement Learning based Renewable Energy Error Compensable Forecasting (Papers Track) Abstract and authors: (click to expand) Abstract: Recently, renewable energy is rapidly integrated into the power grid to prevent climate change, and accurate forecasting of renewable generation becomes critical for reliable power system operation. However, existing forecasting algorithms only focused on reducing forecasting errors without considering error compensability by using a large-scale battery. In this paper, we propose a novel strategy called error compensable forecasting. We switch the objective of forecasting from reducing errors to making errors compensable by leveraging a battery. Specifically, we propose a deep reinforcement learning based framework having forecasting in the loop of control. Extensive simulations show that the proposed one-hour ahead forecasting achieves zero error for more than 98% of time while reducing the operational expenditure by up to 44%. Authors: Jaeik Jeong (Sogang University); Hongseok Kim (Sogang University)	Power & Energy
ICLR 2020	Missing-insensitive Short-term Load Forecasting Leveraging Autoencoder and LSTM (Papers Track) Abstract and authors: (click to expand) Abstract: Short-term load forecasting (STLF) is fundamental for power system operation, demand response, and also greenhouse gas emission reduction. So far, most deep learning-based STLF techniques require intact data, but many real-world datasets contain missing values due to various reasons, and thus missing imputation using deep learning is actively studied. However, missing imputation and STLF have been considered independently so far. In this paper, we jointly consider missing imputation and STLF and propose a family of autoencoder/LSTM combined models to realize missing-insensitive STLF. Specifically, autoencoder (AE), denoising autoencoder (DAE), and convolutional autoencoder (CAE) are investigated for extracting features, which is directly fed into the input of LSTM. Our results show that three proposed autoencoder/LSTM combined models significantly improve forecasting accuracy compared to the baseline models of deep neural network and LSTM. Furthermore, the proposed CAE/LSTM combined model outperforms all other models for 5%-25% of random missing data. Authors: Kyungnam Park (Sogang University); Jaeik Jeong (Sogang University); Hongseok Kim (Sogang University)	Power & Energy
ICLR 2020	A Machine Learning Pipeline to Predict Vegetation Health (Papers Track) Abstract and authors: (click to expand) Abstract: Agricultural droughts can exacerbate poverty and lead to famine. Timely distribution of disaster relief funds is essential to help minimise the impact of drought. Indices of vegetation health are indicative of higher risk of agricultural drought, but their prediction remains challenging, particularly in Africa. Here, we present an open-source machine learning pipeline for climate-related data. Specifically, we train and analyse a recurrent model to predict pixel-wise vegetation health in Kenya. Authors: Thomas Lees (University of Oxford); Gabriel Tseng (Okra Solar); Simon Dadson (University of Oxford); Alex Hernández (University of Osnabrück); Clement G. Atzberger (University of Natural Resources and Life Sciences); Steven Reece (University of Oxford)	Disaster Management and Relief Extreme Weather
ICLR 2020	Understanding the dynamics of climate-crucial food choice behaviours using Distributional Semantics (Papers Track) Abstract and authors: (click to expand) Abstract: Developed countries must make swift movements toward plant-based diets in order to mitigate climate change and maintain food security. However, researchers currently lack clear insight into the psychological dimensions that influence food choice, which is necessary to encourage the societal adaptation of new diets. In this project, we use Skip-gram word embeddings trained on the ukWaC corpus as a lens to study the implicit mental representations people have of foods. Our data-driven insights expand on findings from traditional, interview-based studies by uncovering implicit mental representations, allowing a better understanding the complex combination of conscious and sub-conscious processes surrounding food choice. In particular, our findings shed light on the pervasiveness of meat as the ‘centre’ of the meal in the UK. Authors: Claudia Haworth (University of Sheffield); Gabriella Viglioco (University College London)	Societal Adaptation & Resilience Agriculture & Food
ICLR 2020	SolarNet: A Deep Learning Framework to Map Solar Plants In China From Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Renewable energy such as solar power is critical to fight the ever more serious climate, how to effectively detect renewable energy has became an important issue for governments. In this paper, we proposed a deep learning framework named SolarNet which is designed to perform semantic segmentation on large scale satellite imagery data to detect solar farms. SolarNet has successfully mapped 439 solar farms in China, covering near 2000 square kilometers, equivalent to the size of whole Shenzhen city or two and a half of New York city. To the best of our knowledge, it is the first time that we used deep learning to reveal the locations and sizes of solar farms in China, which could provide insights for solar power companies, climate finance and markets. Authors: Xin Hou (WeBank); Biao Wang (WeBank); Wanqi Hu (WeBank); lei yin (WeBank); Anbu Huang (WeBank); Haishan Wu (WeBank)	Power & Energy Ecosystems & Biodiversity
ICLR 2020	Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence (Papers Track) Abstract and authors: (click to expand) Abstract: Deep learning approaches have shown much promise for climate sciences, especially in dimensionality reduction and compression of large datasets. A major issue in deep learning of climate phenomena, like geophysical turbulence, is the lack of physical guarantees. In this work, we propose a general framework to directly embed the notion of incompressible fluids into Convolutional Neural Networks, for coarse-graining of turbulence. These \textbf{physics-embedded neural networks} leverage interpretable strategies from numerical methods and computational fluid dynamics to enforce physical laws and boundary conditions by taking advantage the mathematical properties of the underlying equations. We demonstrate results on 3D fully-developed turbulence, showing that the \textit{physics-aware inductive bias} drastically improves local conservation of mass, without sacrificing performance according to several other metrics characterizing the fluid flow. Authors: Arvind T Mohan (Los Alamos National Laboratory); NIcholas Lubbers (Los Alamos National Laboratory); Daniel Livescu (Los Alamos National Laboratory); Misha Chertkov (University of Arizona)	Climate Science & Modeling Extreme Weather
ICLR 2020	DETECTION OF HOUSING AND AGRICULTURE AREAS ON DRY-RIVERBEDS FOR THE EVALUATION OF RISK BY LANDSLIDES USING LOW-RESOLUTION SATELLITE IMAGERY BASED ON DEEP LEARNING. STUDY ZONE: LIMA, PERU (Papers Track) Abstract and authors: (click to expand) Abstract: The expansion of human settlements in Peru has caused risk exposure to landslides. However, this risk could increase because the intensity of the El niño phenomenon will be greater in the coming years, increasing rainfall on the Peruvian coast. In this paper, we present a novel methodology for detecting housing areas and agricultural lands in low-resolution satellite imagery in order to analyze potential risk in case of unexpected landslides. It was developed by creating two datasets from Lima Metropolitana in Peru, one of which is for detecting dry riverbeds and agriculture lands, and the other for classifying housing areas. We applied data augmentation based on geometrical methods and trained architectures based on U-net methods separately and then, overlap the results for risk assessment. We found that there are areas with significant potential risk that have been classified by the Peruvian government as medium or low risk areas. On this basis, it is recommended obtain a dataset with better resolution that can identify how many housing areas will be affected and take the appropriate prevention measures. Further research in post-processing is needed for suppress noise in our results. Authors: Brian Cerrón (National University of Engineering); Cristopher Bazan (National University of Engineering); Alberto Coronado (National University of Engineering)	Buildings Climate Science & Modeling Heavy Industry and Manufacturing Land Use
ICLR 2020	Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals (Papers Track) Abstract and authors: (click to expand) Abstract: The United Nations' ambitions to combat climate change and prosper human development are manifested in the Paris Agreement and the Sustainable Development Goals (SDGs), respectively. These are inherently inter-linked as progress towards some of these objectives may accelerate or hinder progress towards others. We investigate how these two agendas influence each other by defining networks of 18 nodes, consisting of the 17 SDGs and climate change, for various groupings of countries. We compute a non-linear measure of conditional dependence, the partial distance correlation, given any subset of the remaining 16 variables. These correlations are treated as weights on edges, and weighted eigenvector centralities are calculated to determine the most important nodes. We find that SDG 6, clean water and sanitation, and SDG 4, quality education, are most central across nearly all groupings of countries. In developing regions, SDG 17, partnerships for the goals, is strongly connected to the progress of other objectives in the two agendas whilst, somewhat surprisingly, SDG 8, decent work and economic growth, is not as important in terms of eigenvector centrality. Authors: Felix Laumann (Imperial College London); Julius von Kügelgen (MPI for Intelligent Systems, Tübingen & University of Cambridge); Mauricio Barahona (Imperial College London)	Climate Policy Societal Adaptation & Resilience
ICLR 2020	A CONTINUAL LEARNING APPROACH FOR LOCAL LEVEL ENVIRONMENTAL MONITORING IN LOW-RESOURCE SETTINGS (Papers Track) Abstract and authors: (click to expand) Abstract: An increasingly important dimension in the quest for mitigation and monitoring of environmental change is the role of citizens. The crowd-based monitoring of local level anthropogenic alterations is essential towards measurable changes in different contributing factors to climate change. With the proliferation of mobile technologies here in the African continent, it is useful to have machine learning based models that are deployed on mobile devices and that can learn continually from streams of data over extended time, possibly pertaining to different tasks of interest. In this paper, we demonstrate the localisation of deforestation indicators using lightweight models and extend to incorporate data about wildfires and smoke detection. The idea is to show the need and potential of continual learning approaches towards building robust models to track local environmental alterations. Authors: Arijit Patra (University of Oxford)	Disaster Management and Relief Ecosystems & Biodiversity Societal Adaptation & Resilience Forests
ICLR 2020	WeatherBench: A benchmark dataset for data-driven weather forecasting (Papers Track) Best Paper Award Abstract and authors: (click to expand) Abstract: Accurate weather forecasts are a crucial prerequisite for climate change adaptation. Can these be provided by deep learning? First studies show promise, but the lack of a common dataset and evaluation metrics make inter-comparison between the proposed models difficult. In fact, despite the recent research surge in data-driven weather forecasting, there is currently no standard approach for evaluating the proposed models. Here we introduce WeatherBench, a benchmark dataset for data-driven medium-range weather forecasting. We provide data derived from an archive of assimilated earth observations for the last 40 years that has been processed to facilitate the use in machine learning models. We propose a simple and clear evaluation metric which will enable a direct comparison between different proposed methods. Further, we provide baseline scores from simple linear regression techniques, purely physical forecasting models as well as existing deep learning weather forecasting models. All data and code are made publicly available along with tutorials for getting started. We believe WeatherBench will provide a useful and reproducible way of evaluating data-driven weather forecasting models and we hope that it will accelerate research in this direction. Authors: Stephan Rasp (Technical University of Munich); Soukayna Mouatadid (University of Toronto); Peter Dueben (European Centre for Medium-Range Weather Forecasts (ECMWF)); Sebastian Scher (Stockholm University); Jonathan Weyn (University of Washington); Nils Thuerey (nils.thuerey@tum.de)	Climate Science & Modeling Extreme Weather
ICLR 2020	Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks (Papers Track) Abstract and authors: (click to expand) Abstract: We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large scale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization. Authors: Victor Schmidt (Mila); Mustafa Alghali Muhammed (University of Khartoum); Kris Sankaran (Montreal Institute for Learning Algorithms); Tianle Yuan (NASA); Yoshua Bengio (Mila)	Climate Science & Modeling
ICLR 2020	SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework (Papers Track) Abstract and authors: (click to expand) Abstract: Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off. As climate change increases the likelihood of extreme weather events and reduces the predictability of weather, and non-optimal soil moistures for crops may become more likely. In this work, we use a series of LSTM architectures to analyze measurements of soil moisture and vegetation indices derived from satellite imagery. The system learns to predict the future values of these measurements. These spatially sparse values and indices are used as input features to an interpolation method that infer spatially dense moisture maps at multiple depths for a future time point. This has the potential to provide advance warning for soil moistures that may be inhospitable to crops across an area with limited monitoring capacity. Authors: Conrad J Foley (Deep Planet); Sagar Vaze (deepplanet.ai); Mohamed El Amine Seddiq (Deep Planet); Aleksei Unagaev (Deep Planet); Natalia Efremova (University of Oxford)	Ecosystems & Biodiversity Extreme Weather Agriculture & Food
ICLR 2020	Prediction of Bayesian Intervals for Tropical Storms (Papers Track) Abstract and authors: (click to expand) Abstract: Building on recent research for prediction of hurricane trajectories using recurrent neural networks (RNNs), we have developed improved methods and generalized the approach to predict a confidence interval region of the trajectory utilizing Bayesian methods. Tropical storms are capable of causing severe damage, so accurately predicting their trajectories can bring significant benefits to cities and lives, especially as they grow more intense due to climate change effects. By implementing the Bayesian confidence interval using dropout in an RNN, we improve the actionability of the predictions, for example by estimating the areas to evacuate in the landfall region. We used an RNN to predict the trajectory of the storms at 6-hour intervals. We used latitude, longitude, windspeed, and pressure features from a Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset of about 500 tropical storms in the Atlantic Ocean. Our results show how neural network dropout values affect our predictions and Bayesian intervals. Authors: Max Chiswick (Independent); Sam Ganzfried (Ganzfried Research)	Extreme Weather Climate Science & Modeling
ICLR 2020	MobilityNet: Towards a Public Dataset for Multi-modal Mobility Research (Papers Track) Abstract and authors: (click to expand) Abstract: Influencing transportation demand can significantly reduce CO2 emissions. Individual user mobility models are key to influencing demand at the personal and structural levels. Constructing such models is a challenging task that depends on a number of interdependent steps. Progress on this task is hamstrung by the lack of high quality public datasets. We introduce MobilityNet: the first step towards a common ground for multi-modal mobility research. MobilityNet solves the holistic evaluation, privacy preservation and fine grained ground truth problems through the use of artificial trips, control phones, and repeated travel. It currently includes 1080 hours of data from both Android and iOS, representing 16 different travel contexts and 4 different sensing configurations. Authors: K. Shankari (UC Berkeley); Jonathan Fürst (NEC Laboratories Europe); Mauricio Fadel Argerich (NEC Laboratories Europe); Eleftherios Avramidis (DFKI GmbH); Jesse Zhang (UC Berkeley)	Transportation Buildings
ICLR 2020	Wavelet-Powered Neural Networks for Turbulence (Papers Track) Abstract and authors: (click to expand) Abstract: One of the fundamental driving phenomena for climate effects is fluid turbulence in geophysical flows. Modeling these flows and explaining its associated spatio-temporal phenomena are notoriously difficult tasks. Navier-Stokes (NS) equations describe all the details of the fluid motions, but require accounting for unfeasibly many degrees of freedom in the regime of developed turbulence. Model reduction and surrogate modeling of turbulence is a general methodology aiming to circumvent this curse of dimensionality. Originally driven by phenomenological considerations, multiple attempts to model-reduce NS equations got a new boost recently with Deep Learning (DL), trained on the ground truth data, e.g. extracted from high-fidelity Direct Numerical Simulations (DNS). However, early attempts of building NNs to model turbulence has also revealed its lack of interpretability as the most significant shortcoming. In this paper we address the key challenge of devising reduced but, at least partially, interpretable model. We take advantage of the balance between strong mathematical foundations and the physical interpretability of wavelet theory to build a spatio-temporally reduced dynamical map which fuses wavelet based spatial decomposition with spatio-temporal modeling based on Convolutional Long Short Term Memory (C-LSTM) architecture. It is shown that the wavelet-based NN makes progress in scaling to large flows, by reducing computational costs and GPU memory requirements. Authors: Arvind T Mohan (Los Alamos National Laboratory); Daniel Livescu (Los Alamos National Laboratory); Misha Chertkov (University of Arizona)	Climate Science & Modeling
ICLR 2020	Benchmarks for Grid Flexibility Prediction: Enabling Progress and Machine Learning Applications (Proposals Track) Abstract and authors: (click to expand) Abstract: Decarbonizing the grid is recognized worldwide as one of the objectives for the next decades. Its success depends on our ability to massively deploy renewable resources, but to fully benefit from those, grid flexibility is needed. In this paper we put forward the design of a benchmark that will allow for the systematic measurement of demand response programs' effectiveness, information that we do not currently have. Furthermore, we explain how the proposed benchmark will facilitate the use of Machine Learning techniques in grid flexibility applications. Authors: Diego Kiedansk (Telecom ParisTech); Lauren Kuntz (Gaiascope); Daniel Kofman (Telecom ParisTech)	Power & Energy
ICLR 2020	Machine Learning Approaches to Safeguarding Continuous Water Supply in the Arid and Semi-arid Lands of Northern Kenya (Proposals Track) Abstract and authors: (click to expand) Abstract: Arid and semi-arid regions (ASALs) in developing countries are heavily affected by the effects of global warming and climate change, leading to adverse climatic conditions such as drought and flooding. This paper explores the problem of fresh-water access in northern Kenya and measures being taken to safeguard water access despite these harsh climatic changes. We present an integrated water management and decision-support platform, eMaji Manager, that we developed and deployed in five ASAL counties in northern Kenya to manage waterpoint access for people and livestock. We then propose innovative machine learning methods for understanding waterpoint usage and repair patterns for sensor-instrumented waterpoints (e.g., boreholes). We explore sub-sequence discriminatory models and recurrent neural networks to predict water-point failures, improve repair response times and ultimately support continuous access to water. Authors: Fred Otieno (IBM); Timothy Nyota (IBM); Isaac Waweru (IBM); Celia Cintas (IBM Research); Samuel C Maina (IBM Research); William Ogallo (IBM Research); Aisha Walcott-Bryant (IBM Research - Africa)	Climate Science & Modeling Societal Adaptation & Resilience
ICLR 2020	Accelerated Data Discovery for Scalable Climate Action (Proposals Track) Abstract and authors: (click to expand) Abstract: According to the Intergovernmental Panel on Climate Change (IPCC), the planet must decarbonize by 50% by 2030 in order to keep global warming below 1.5C. This goal calls for a prompt and massive deployment of solutions in all societal sectors - research, governance, finance, commerce, health care, consumption. One challenge for experts and non-experts is access to the rapidly growing body of relevant information, which is currently scattered across many weakly linked domains of expertise. We propose a large-scale, semi-automatic, AI-based discovery system to collect, tag, and semantically index this information. The ultimate goal is a near real-time, partially curated data catalog of global climate information for rapidly scalable climate action. Authors: Henning Schwabe (Private); Sumeet Sandhu (Elementary IP LLC); Sergy Grebenschikov (Private)	Climate Science & Modeling Climate Finance & Economics Heavy Industry and Manufacturing Climate Policy
ICLR 2020	YOU FORGOT IT IN THE GENOTYPE, MODELING TOWARDS ADAPTATION OF FOOD CROPS UNDER CLIMATE CHANGE THREAT (Proposals Track) Abstract and authors: (click to expand) Abstract: Agriculture is facing the disastrous effects of frequent drastic climate changes. Efforts have increased towards the implementation of inexpensive solutions for crop-yield prediction using publicly available data to prevent severe long-term problems like food scarcity and security, amongst others. Agricultural productiv- ity is intrinsic to the choice of plant species (i.e. cultivar) and represents oppor- tunity cost for farm managers. The currently used cultivars have been artificially selected for productivity at the expense of not being flexible to survive drastic cli- mate changes. Current state-of-the-art machine learning models have modelled holistically all agricultural counterparts (i.e. soil, management, weather, crop cul- tivars etc), albeit, oversimplifying some of the biological features of their culti- vars without taking advantage of their data properties. Specifically, these models oversimplify some biological features like the genotype making them irrelevant or depicting incomplete conclusions since not all of the information from the cul- tivar is incorporated. With the goal of creating new models that perform well on the yield prediction task in unstable weather conditions (e.g. under the effect of climate change), here the authors argue for the importance of incorporating additional biological features inferred from the genotype, like stability, and hy- pothesise that current state-of-the-art models for grain-yield prediction are blind to such features, and hence not applicable in such scenario. Authors: Olivia Mendivil Ramos (Cold Spring Harbor Laboratory); Linda Petrini (Mila)	Agriculture & Food
ICLR 2020	Hurricane Nowcasting with Irregular Time-step using Neural-ODE and Video Prediction (Proposals Track) Abstract and authors: (click to expand) Abstract: Fast and accurate prediction of extreme climate events is critical especially in the recent globally warming environment. Considering recent advancements in deep neural networks, it is worthwhile to tackle this problem as data-driven spatio-temporal prediction using neural networks. However, a nontrivial challenge in practice lies in irregular time gaps between which climate observation data are collected due to sensor errors and other issues. This paper proposes an approach for spatio-temporal hurricane prediction that can address this issue of irregular time gaps in collected data with a simple but robust end-to-end model based on Neural Ordinary Differential Equation and video prediction model based on Retrospective Cycle-GAN. Authors: Sunghyun Park (KAIST); Kangyeol Kim (KAIST); Sookyung Kim (Lawrence Livermore National Laboratory); Joonseok Lee (Google Research); Junsoo Lee (KAIST); Jiwoo Lee (Lawrence Livermore National Laboratory); Jaegul Choo (KAIST)	Extreme Weather Disaster Management and Relief
ICLR 2020	Indigenous Knowledge Aware Drought Monitoring, Forecasting and Prediction Using Deep Learning Techniques (Proposals Track) Abstract and authors: (click to expand) Abstract: The general objective of this proposed research work is to design deep learning based hybrid comprehensive framework for drought monitoring, forecasting and prediction using scientific and indigenous knowledge as an integration of connectionist and symbolic AI. In Ethiopia, among all extreme climate events, drought is considered as the most complex phenomenon affecting the country and its impact is also high due to absence of locally grounded intelligent and explainable technology-oriented drought early warning and monitoring system. Thus, studying Ethiopic perspective of drought monitoring and prediction in line with continental and global climate change is vital for drought impact minimization and sustainable development of the country. Moreover, having technology assisted early protective, preventative action is also many times cheaper than the associated response to humanitarian crisis. Accordingly, this proposed work will have different expected outputs, including: drought risk identification, drought monitoring, drought preparedness, drought forecasting, drought mitigation, and post drought best practice recommendation models with interactive visualizations and explanations. Authors: Kidane W Degefa (Haramaya University)	Disaster Management and Relief Extreme Weather
ICLR 2020	TrueBranch: Metric Learning-based Verification of Forest Conservation Projects (Proposals Track) Best Proposal Award Abstract and authors: (click to expand) Abstract: International stakeholders increasingly invest in offsetting carbon emissions, for example, via issuing Payments for Ecosystem Services (PES) to forest conservation projects. Issuing trusted payments requires a transparent monitoring, reporting, and verification (MRV) process of the ecosystem services (e.g., carbon stored in forests). The current MRV process, however, is either too expensive (on-ground inspection of forest) or inaccurate (satellite). Recent works propose low-cost and accurate MRV via automatically determining forest carbon from drone imagery, collected by the landowners. The automation of MRV, however, opens up the possibility that landowners report untruthful drone imagery. To be robust against untruthful reporting, we propose TrueBranch, a metric learning-based algorithm that verifies the truthfulness of drone imagery from forest conservation projects. TrueBranch aims to detect untruthfully reported drone imagery by matching it with public satellite imagery. Preliminary results suggest that nominal distance metrics are not sufficient to reliably detect untruthfully reported imagery. TrueBranch leverages a method from metric learning to create a feature embedding in which truthfully and untruthfully collected imagery is easily distinguishable by distance thresholding. Authors: Simona Santamaria (ETH Zurich); David Dao (ETH Zurich); Björn Lütjens (MIT); Ce Zhang (ETH)	Carbon Capture & Sequestration Climate Finance & Economics Ecosystems & Biodiversity Forests
ICLR 2020	Advancing Renewable Electricity Consumption With Reinforcement Learning (Proposals Track) Abstract and authors: (click to expand) Abstract: As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions. Authors: Filip Tolovski (Fraunhofer Heinrich-Hertz-Institut)	Power & Energy Heavy Industry and Manufacturing
ICLR 2020	Xingu: Explaining critical geospatial predictions in weak supervision for climate finance (Proposals Track) Abstract and authors: (click to expand) Abstract: Monitoring, Reporting, and Verification (MRV) play a crucial key role in the decision-making of climate investors, policymakers and conservationists. Remote sensing is commonly used for MRV but practical solutions are constrained by a lack of labels to train machine learning-based downstream tasks. Recent work leverages weak supervision to alleviate the problem of labelled data scarcity. However, the definition of weak supervision signals is limited by the existence of millions of possible heuristic-based feature generation rules. Furthermore, these rules are often difficult to interpret for climate finance and underperform in critical data subsets. We propose Xingu, an interpretable MRV system to explain weak supervision rules using game-theoretic SHAP values for critical model predictions. Moreover, Xingu enables domain experts to collectively design and share labelling functions, thus curating a reusable knowledge base for weak supervision signals. Authors: David Dao (ETH Zurich); Johannes Rausch (ETH Zurich); Ce Zhang (ETH); Iveta Rott (ETH Zurich)	Climate Finance & Economics
ICLR 2020	Towards a unified standards for smart infrastructure datasets (Proposals Track) Abstract and authors: (click to expand) Abstract: Development of smart devices and smart home appliances allowed us to harness more data about energy patterns inside households, overtime this amount will increase. There are contributions published to address building datasets, working for objective of energy consumption optimization. Yet there are still factors if included could help in understanding problem better. This proposal tries to annotate missing features that if applied could help in a better understanding energy consumption in smart buildings impact on environment. Second, to have a unified standards that help different solutions to be compared properly. Authors: Abdulrahman A Ahmed (Cairo University)	Buildings Power & Energy
ICLR 2020	MACHINE LEARNING APPLICATIONS THAT CAN HELP PASTORAL COMMUNITIES IN NORTHERN KENYA AND ELSEWHERE ADAPT TO CLIMATE CHANGE (Proposals Track) Abstract and authors: (click to expand) Abstract: I propose the use of Machine Learning techniques such as Active Learning(AL) and Transfer Learning(TL) to translate climate information and adaption technique from major Western and Asian languages to thousands of low resource languages in the developing world. Studies have shown that access to information can help people assess the magnitude of the climate change challenge, possible options and those feasible within the relevant context (Nyahunda & Tiri-vangasi, 2019) I endeavor to demonstrate that if this information was available in a language the locals can understand, it would result in local empowerment and as a result inspire action. Authors: Jefferson Sankara (Lori Systems)	Societal Adaptation & Resilience
ICLR 2020	Nutrient demand, Risk and Climate change: Evidence from historical rice yield trials in India (Proposals Track) Abstract and authors: (click to expand) Abstract: We use data from historical agronomic fertilizer trials to identify the effect of climate change on the average rice yield and the yield variability in India. The contribution of this paper is three folds: firstly, it has a methodological contribution by modelling the input conditional yield densities using a stochastic production structure for a developing country like India. In doing so, it predicts and measure the effects of climate change on rice grown in tropical regions; secondly,it estimates the nutrient demand and its link with the climate change; thirdly, by modelling the yield uncertainty, it characterizes the risk and role for insurance as a tool for tackling climate change in the developing countries. Authors: Sandip K Agarwal (IISER Bhopal)	Climate Policy Agriculture & Food
ICLR 2020	USING MACHINE LEARNING TO ANALYZE CLIMATE CHANGE TECHNOLOGY TRANSFER (CCTT) (Proposals Track) Abstract and authors: (click to expand) Abstract: The objective of the present paper is to review the climate change technology transfer. This research proposes a method for analysing CCTT using patent analysis and topic modelling. A collection of climate change mitigation related technology (CCMT) patents from patent databases would be used as input to group patents in several relevant topics for climate change mitigation using the topic exploration model in this research. The research questions we want to address are: how have the patenting activities changed over time in CCMT patents? And who are the technological leaders? The investigation of these questions can offer the technological landscape in climate change-related technologies at the international level. We propose a hybrid Latent Dirichlet Allocation (LDA) approach for topic modelling and identification of relationships between terms and topics related to CCMT, enabling better visualizations of underlying intellectual property dynamics. Further, we propose predictive modelling for CCTT and competitor analysis to identify and rank countries with a similar patent landscape. The projected results are expected to facilitate the transfer process associated with existing and emerging climate change technologies and improve technology cooperation between governments. Authors: Shruti Kulkarni (Indian Institute of Science (IISc))	Climate Finance & Economics Societal Adaptation & Resilience
ICLR 2020	Using ML to close the vocabulary gap in the context of environment and climate change in Chichewa (Proposals Track) Abstract and authors: (click to expand) Abstract: In the west, alienation from nature and deteriorating opportunities to experience it, have led educators to incorporate educational programs in schools, to bring pupils in contact with nature and to enhance their understanding of issues related to the environment and its protection. In Africa, and in Malawi, where most people engage in agriculture, and spend most of their time in the 'outdoors', alienation from nature is happening too, although in different ways. Large portion of the indigenous vocabulary and knowledge remains unknown or is slowly disappearing, and there is a need to build a glossary of terms regarding environment and climate change in the vernacular to improve the dialog regarding climate change and environmental protection.. We believe that ML has a role to play in closing the ‘vocabulary gap’ of terms and concepts regarding the environment and climate change that exists in Chichewa and other Malawian languages by helping to creating a visual dictionary of key terms used to describe the environment and explain the issues involved in climate change and their meaning. Chichewa is a descriptive language, one English term may be translated using several words. Thus, the task is not to detect just literal translations, but also translations by means of ‘descriptions’ and illustrations and thus extract correspondence between terms and definitions and to measure how appropriate a term is to convey the meaning intended. As part of this project, ML can be used to identify ‘loanword patterns’, which may be useful in understanding the transmission of cultural items. Authors: Amelia Taylor (University of Malawi, The Polytechnic)	Ecosystems & Biodiversity Climate Science & Modeling Forests Land Use Agriculture & Food
NeurIPS 2019	Warm-Starting AC Optimal Power Flow with Graph Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Efficient control of power grids is important both for efficiently managing genera- tors and to prolong longevity of components. However, that problem is NP-hard and linear approximations are necessary. The deployment of machine learning methods is hampered by the need to guarantee solutions. We propose to use Graph Neural Networks (GNNs) to model a power grid and produce an initial solution used to warm-start the optimization. This allows us to achieve the best of both worlds: Fast convergence and guaranteed solutions. On a synthetic power grid modelling Texas, we achieve a mean speedup by a factor of 2.8. This allows us to dispense with linear approximation, leads to more efficient generator dispatch, and can potentially save hundreds of megatons of CO2 -equivalent. Authors: Frederik Diehl (fortiss)	Power & Energy
NeurIPS 2019	Quantifying Urban Canopy Cover with Deep Convolutional Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: Urban canopy cover is important to mitigate the impact of climate change. Yet, existing quantification of urban greenery is either manual and not scalable, or use traditional computer vision methods that are inaccurate. We train deep convolutional neural networks (DCNNs) on datasets used for self-driving cars to estimate urban greenery instead, and find that our semantic segmentation and direct end-to-end estimation method are more accurate and scalable, reducing mean absolute error of estimating the Green View Index (GVI) metric from 10.1% to 4.67%. With the revised DCNN methods, the Treepedia project was able to scale and analyze canopy cover in 22 cities internationally, sparking interest and action in public policy and research fields. Authors: Bill Cai (Massachusetts Institute of Technology); Xiaojiang Li (Temple University); Carlo Ratti (Massachusetts Institute of Technology )	Buildings Land Use
NeurIPS 2019	Using LSTMs for climate change assessment studies on droughts and floods (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change affects occurrences of floods and droughts worldwide. However, predicting climate impacts over individual watersheds is difficult, primarily because accurate hydrological forecasts require models that are calibrated to past data. In this work we present a large-scale LSTM-based modeling approach that - by training on large data sets - learns a diversity of hydrological behaviors. Previous work shows that this model is more accurate than current state-of-the-art models, even when the LSTM-based approach operates out-of-sample and the latter in-sample. In this work, we show how this model can assess the sensitivity of the underlying systems with regard to extreme (high and low) flows in individual watersheds over the continental US. Authors: Frederik Kratzert (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Daniel Klotz (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Johannes Brandstetter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Pieter-Jan Hoedt (Johannes Kepler University Linz); Grey Nearing (Department of Geological Sciences, University of Alabama, Tuscaloosa, AL United States); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)	Climate Science & Modeling
NeurIPS 2019	Learning to Focus and Track Hurricanes (Papers Track) Abstract and authors: (click to expand) Abstract: This paper tackles the task of extreme climate event tracking. We propose a simple but robust end-to-end model based on multi-layered ConvLSTMs, suitable for climate event tracking. It first learns to imprint the location and the appearance of the target at the first frame in an auto-encoding fashion. Next, the learned feature is fed to the tracking module to track the target in subsequent time frames. To tackle the data shortage problem, we propose data augmentation based on conditional generative adversarial networks. Extensive experiments show that the proposed framework significantly improves tracking performance of a hurricane tracking task over several state-of-the-art methods. Authors: Sookyung Kim (Lawrence Livermore National Laboratory); Sunghyun Park (Korea University); Sunghyo Chung (Kakao Corp.); Joonseok Lee (Google Research); Jaegul Choo (Korea University); Mr Prabhat (Lawrence Berkeley National Laboratory); Yunsung Lee (Korea University)	Extreme Weather Climate Science & Modeling
NeurIPS 2019	DeepWind: Weakly Supervised Localization of Wind Turbines in Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Wind energy is being adopted at an unprecedented rate. The locations of wind energy sources, however, are largely undocumented and expensive to curate manually, which significantly impedes their integration into power systems. Towards the goal of mapping global wind energy infrastructure, we develop deep learning models to automatically localize wind turbines in satellite imagery. Using only image-level supervision, we experiment with several different weakly supervised convolutional neural networks to detect the presence and locations of wind turbines. Our best model, which we call DeepWind, achieves an average precision of 0.866 on the test set. DeepWind demonstrates the potential of automated approaches for identifying wind turbine locations using satellite imagery, ultimately assisting with the management and adoption of wind energy worldwide. Authors: Sharon Zhou (Stanford University); Jeremy Irvin (Stanford); Zhecheng Wang (Stanford University); Ram Rajagopal (Stanford University); Andrew Ng (Stanford U.); Eva Zhang (Stanford University); Will Deaderick (Stanford University); Jabs Aljubran (Stanford University)	Power & Energy
NeurIPS 2019	Streamflow Prediction with Limited Spatially-Distributed Input Data (Papers Track) Abstract and authors: (click to expand) Abstract: Climate change causes more frequent and extreme weather phenomena across the globe. Accurate streamflow prediction allows for proactive and mitigative action in some of these events. As a first step towards models that predict streamflow in watersheds for which we lack ground truth measurements, we explore models that work on spatially-distributed input data. In such a scenario, input variables are more difficult to acquire, and thus models have access to limited training data. We present a case study focusing on Lake Erie, where we find that tree-based models can yield more accurate predictions than both neural and physically-based models. Authors: Martin Gauch (University of Waterloo); Juliane Mai (University of Waterloo); Shervan Gharari (University of Saskatchewan); Jimmy Lin (University of Waterloo)	Climate Science & Modeling Disaster Management and Relief Extreme Weather
NeurIPS 2019	Establishing an Evaluation Metric to Quantify Climate Change Image Realism (Papers Track) Abstract and authors: (click to expand) Abstract: With success on controlled tasks, generative models are being increasingly applied to humanitarian applications. In this paper, we focus on the evaluation of a conditional generative model that illustrates the consequences of climate change-induced flooding to encourage public interest and awareness on the issue. Because metrics for comparing the realism of different modes in a conditional generative model do not exist, we propose several automated and human-based methods for evaluation. To do this, we adapt several existing metrics, and assess the automated metrics against gold standard human evaluation. We find that using Frechet Inception Distance (FID) with embeddings from an intermediary Inception-V3 layer that precedes the auxiliary classifier produces results most correlated with human realism. While insufficient alone to establish a human-correlated automatic evaluation metric, we believe this work begins to bridge the gap between human and automated generative evaluation procedures. Authors: Sharon Zhou (Stanford University); Sasha Luccioni (Mila); Gautier Cosne (Mila); Michael Bernstein (Stanford University); Yoshua Bengio (Mila)	Societal Adaptation & Resilience
NeurIPS 2019	Energy Usage Reports: Environmental awareness as part of algorithmic accountability (Papers Track) Abstract and authors: (click to expand) Abstract: The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also include comparisons with energy mixtures employed in electrical grids around the world. We propose including these automatically-generated Energy Usage Reports as part of standard algorithmic accountability practices, and demonstrate the use of these reports as part of model-choice in a machine learning context. Authors: Kadan Lottick (Haverford College); Silvia Susai (Haverford College); Sorelle Friedler (Haverford College); Jonathan Wilson (Haverford College)
NeurIPS 2019	Natural Language Generation for Operations and Maintenance in Wind Turbines (Papers Track) Abstract and authors: (click to expand) Abstract: Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated costs. Machine learning has been applied to predict faults in wind turbines, but these predictions have not been supported by suggestions on how to avert and fix occurring errors. In this paper, we present a data-to-text generation system utilising transformers to produce event descriptions of turbine faults from SCADA data capturing the operational status of turbines, and proposing maintenance strategies. Experiments show that our model learns reasonable feature representations that correspond to expert judgements. We anticipate that in making a contribution to the reliability of wind energy, we can encourage more organisations to switch to sustainable energy sources and help combat climate change. Authors: Joyjit Chatterjee (University of Hull); Nina Dethlefs (University of Hull)	Power & Energy Ecosystems & Biodiversity
NeurIPS 2019	Make Thunderbolts Less Frightening — Predicting Extreme Weather Using Deep Learning (Papers Track) Abstract and authors: (click to expand) Abstract: Forecasting severe weather conditions is still a very challenging and computationally expensive task due to the enormous amount of data and the complexity of the underlying physics. Machine learning approaches and especially deep learning have however shown huge improvements in many research areas dealing with large datasets in recent years. In this work, we tackle one specific sub-problem of weather forecasting, namely the prediction of thunderstorms and lightning. We propose the use of a convolutional neural network architecture inspired by UNet++ and ResNet to predict thunderstorms as a binary classification problem based on satellite images and lightnings recorded in the past. We achieve a probability of detection of more than 94% for lightnings within the next 15 minutes while at the same time minimizing the false alarm ratio compared to previous approaches. Authors: Christian Schön (Saarland Informatics Campus); Jens Dittrich (Saarland University)	Extreme Weather Climate Science & Modeling
NeurIPS 2019	Cumulo: A Dataset for Learning Cloud Classes (Papers Track) Best Paper Award Abstract and authors: (click to expand) Abstract: One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this uncertainty is to accurately classify cloud types at high spatial and temporal resolution. In this paper, we introduce Cumulo, a benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width `tracks' of CloudSat cloud labels. Bringing these complementary datasets together is a crucial first step, enabling the Machine-Learning community to develop innovative new techniques which could greatly benefit the climate community. To showcase Cumulo, we provide baseline performance analysis using an invertible flow generative model (IResNet), which further allows us to discover new sub-classes for a given cloud class by exploring the latent space. Authors: Valentina Zantedeschi (Jean Monnet University); Fabrizio Falasca (Georgia Institute of Technology); Alyson Douglas (University of Wisconsin Madison); Richard Strange (University of Oxford); Matt Kusner (University College London); Duncan Watson-Parris (University of Oxford)	Climate Science & Modeling
NeurIPS 2019	Targeting Buildings for Energy Retrofit Using Recurrent Neural Networks with Multivariate Time Series (Papers Track) Abstract and authors: (click to expand) Abstract: The existing building stock accounts for over 30% of global carbon emissions and energy demand. Effective building retrofits are therefore vital in reducing global emissions. Current methods for building energy assessment typically rely on walk-throughs, surveys or the collection of in-situ measurements, none of which are scalable or cost effective. Supervised machine learning methods have the potential to overcome these issues, but their application to retrofit analysis has been limited. This paper serves as a novel showcase for how multivariate time series analysis with Gated Recurrent Units can be applied to targeted retrofit analysis via two case studies: (1) classification of building heating system type and (2) prediction of building envelope thermal properties. Authors: Gaby Baasch (University of Victoria)	Buildings
NeurIPS 2019	Coupling Oceanic Observation Systems to Study Mesoscale Ocean Dynamics (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horizontally sparse, knowledge of the ocean interior thermal structure. The objective of this paper is to develop a methodology to combine these complementary ocean observing systems measurements to obtain a three-dimensional time series of ocean temperatures with high horizontal and vertical resolution. Within an observation-driven framework, we investigate the extent to which mesoscale ocean dynamics in the North Atlantic region may be decomposed into a mixture of dynamical modes, characterized by different local regressions between Sea Surface Temperature (SST), Sea Level Anomalies (SLA) and Vertical Temperature fields. Ultimately we propose a Latent-class regression method to improve prediction of vertical ocean temperature. Authors: Gautier Cosne (Mila); Pierre Tandeo (IMT-Atlantique); Guillaume Maze (Ifremer)	Climate Science & Modeling
NeurIPS 2019	Background noise trends and the detection of calving events in a glacial fjord (Papers Track) Abstract and authors: (click to expand) Abstract: Predicting future sea levels depends on accurately estimating the rate at which ice sheets deliver fresh water and ice to the oceans, and projecting rates of iceberg calving will be improved with more observations of calving events. The background noise environment in a glacial fjord was measured and the data were analyzed. This paper includes an analysis of methods useful for evaluating background noise. It explores the utility of spectral probability density in evaluating background noise characteristics in the frequency domain, models probability density functions of spectral levels and introduces a parameter \(\sigma_T\) that quantifies the character of noise in frequency bands of interest. It also explores the utility of k-medoids clustering as a pre-sorting method to inform the selection of features on which to base the training of more complex algorithms. Authors: Dara Farrell (Graduate of University of Washington)	Climate Science & Modeling
NeurIPS 2019	Reducing Inefficiency in Carbon Auctions with Imperfect Competition (Papers Track) Abstract and authors: (click to expand) Abstract: We study auctions for carbon licenses, a policy tool used to control the social cost of pollution. Each identical license grants the right to produce a unit of pollution. Each buyer (i.e., firm that pollutes during the manufacturing process) enjoys a decreasing marginal value for licenses, but society suffers an increasing marginal cost for each license distributed. The seller (i.e., the government) can choose a number of licenses to auction, and wishes to maximize societal welfare: the total economic value of the buyers minus the social cost. Motivated by emission license markets deployed in practice, we focus on uniform price auctions with a price floor and/or price ceiling. The seller has distributional information about the market, and their goal is to tune the auction parameters to maximize expected welfare. The target benchmark is the maximum expected welfare achievable by any such auction under truth-telling behavior. Unfortunately, the uniform price auction is not truthful, and strategic behavior can significantly reduce (even below zero) the welfare of a given auction configuration. We use tools from theoretical computer science and algorithmic game theory to address the strategic vulnerabilities of these auctions. We describe a subclass of "safe-price" auctions for which the welfare at any Bayes-Nash equilibrium will approximate the welfare under truth-telling behavior. We then show that the better of a safe-price auction, or a truthful auction that allocates licenses to only a single buyer, will approximate the target benchmark. In particular, we show how to choose a number of licenses and a price floor so that the worst-case welfare, at any equilibrium, is a constant approximation to the best achievable welfare under truth-telling after excluding the welfare contribution of a single buyer. This provides a concrete recommendation for how to set the auction parameters in practice in order to achieve guarantees, even in the face of strategic participants. Authors: Kira Goldner (Columbia University); Nicole Immorlica (Microsoft Research); Brendan Lucier (Microsoft Research New England)	Carbon Capture & Sequestration
NeurIPS 2019	Reduction of the Optimal Power Flow Problem through Meta-Optimization (Papers Track) Abstract and authors: (click to expand) Abstract: We introduce a method for solving Optimal Power Flow (OPF) using meta-optimization, which can substantially reduce solution times. A pre-trained classifier that predicts the binding constraints of the system is used to generate an initial reduced OPF problem, defined by removing the predicted non-binding constraints. Through an iterative procedure, this initial set of constraints is then ex- tended by those constraints that are violated but not represented in the reduced OPF, guaranteeing an optimal solution of the original OPF problem with the full set of constraints. The classifier is trained using a meta-loss objective, defined by the computational cost of the series of reduced OPF problems. Authors: Letif Mones (Invenia Labs); Alex Robson (Invenia Labs); Mahdi Jamei (Invenia Labs); Cozmin Ududec (Invenia Labs)	Power & Energy Optimization
NeurIPS 2019	Human-Machine Collaboration for Fast Land Cover Mapping (Papers Track) Abstract and authors: (click to expand) Abstract: We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model's predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. Our hypothesis is that this rich feedback allows human labelers to create mental models that enable them to better choose which biases to introduce to the model. We implement this framework for fine-tuning high-resolution land cover segmentation models and evaluate it against traditional active learning based approaches. More specifically, we fine-tune a deep neural network -- trained to segment high-resolution aerial imagery into different land cover classes in Maryland, USA -- to a new spatial area in New York, USA. We find that the tight loop turns the algorithm and the human operator into a hybrid system that can produce land cover maps of large areas more efficiently than the traditional workflows. Authors: Caleb Robinson (Georgia Institute of Technology); Anthony Ortiz (University of Texas at El Paso); Nikolay Malkin (Yale University); Blake Elias (Microsoft); Andi Peng (Microsoft); Dan Morris (Microsoft); Bistra Dilkina (University of Southern California); Nebojsa Jojic (Microsoft Research)	Ecosystems & Biodiversity Land Use
NeurIPS 2019	A User Study of Perceived Carbon Footprint (Papers Track) Abstract and authors: (click to expand) Abstract: We propose a statistical model to understand people’s perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we design a system to probe people’s perception from simple pairwise comparisons of the relative carbon footprint of their actions. The formulation of the model enables us to take an active-learning approach to selecting the pairs of actions that are maximally informative about the model parameters. We define a set of 18 actions and collect a dataset of 2183 comparisons from 176 users on a university campus. The early results reveal promising directions to improve climate communication and enhance climate mitigation. Authors: Victor Kristof (EPFL); Valentin Quelquejay-Leclere (EPFL); Robin Zbinden (EPFL); Lucas Maystre (Spotify); Matthias Grossglauser (École Polytechnique Fédérale de Lausanne (EPFL)); Patrick Thiran (EPFL)	Societal Adaptation & Resilience
NeurIPS 2019	Design, Benchmarking and Graphical Lasso based Explainability Analysis of an Energy Game-Theoretic Framework (Papers Track) Abstract and authors: (click to expand) Abstract: Energy use in buildings account for approximately half of global electricity consumption and a significant amount of CO2 emissions. The occupants of a building typically lack the independent motivation necessary to optimize their energy usage. In this paper, we propose a novel energy game-theoretic framework for smart building which incorporates human-in-the-loop modeling by creating an interface to allow interaction with occupants and potentially incentivize energy efficient behavior. We present open-sourced dataset and benchmarked results for forecasting of energy resource usage patterns by leveraging classical machine learning and deep learning methods including deep bi-directional recurrent neural networks. Finally, we use graphical lasso to demonstrate the explainable nature on human decision making towards energy usage inherent in the dataset. Authors: Hari Prasanna Das (UC Berkeley ); Ioannis C. Konstantakopoulos (UC Berkeley); Aummul Baneen Manasawala (UC Berkeley); Tanya Veeravalli (UC Berkeley); Huihan Liu (UC Berkeley ); Costas J. Spanos (University of California at Berkeley)	Buildings Power & Energy
NeurIPS 2019	Predicting ice flow using machine learning (Papers Track) Abstract and authors: (click to expand) Abstract: Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniques from unsupervised learning of future video frame prediction, to increase the accuracy of ice flow tracking in multi-spectral satellite images. As the volume of cryosphere data increases in coming years, this is an interesting and important opportunity for machine learning to address a global challenge for climate change, risk management from floods, and conserving freshwater resources. Future frame prediction of ice melt and tracking the optical flow of ice dynamics presents modeling difficulties, due to uncertainties in global temperature increase, changing precipitation patterns, occlusion from cloud cover, rapid melting and glacier retreat due to black carbon aerosol deposition, from wildfires or human fossil emissions. We show the adversarial learning method helps improve the accuracy of tracking the optical flow of ice dynamics compared to existing methods in climate science. We present a dataset, IceNet, to encourage machine learning research and to help facilitate further applications in the areas of cryospheric science and climate change. Authors: Yimeng Min (Mila); Surya Karthik Mukkavilli (Mila); Yoshua Bengio (Mila)	Climate Science & Modeling
NeurIPS 2019	DeepClimGAN: A High-Resolution Climate Data Generator (Papers Track) Abstract and authors: (click to expand) Abstract: Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally intensive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint probability distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves. Authors: Alexandra Puchko (Western Washington University); Brian Hutchinson (Western Washington University); Robert Link (Joint Global Change Research Institute)	Climate Science & Modeling Extreme Weather
NeurIPS 2019	Quantifying the Carbon Emissions of Machine Learning (Papers Track) Abstract and authors: (click to expand) Abstract: From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners as well as organizations can take to mitigate their carbon emissions. Authors: Sasha Luccioni (Mila); Victor Schmidt (Mila); Alexandre Lacoste (Element AI); Thomas Dandres (Polytechnique Montreal)	Societal Adaptation & Resilience Power & Energy
NeurIPS 2019	Measuring Impact of Climate Change on Tree Species: analysis of JSDM on FIA data (Papers Track) Honorable Mention Abstract and authors: (click to expand) Abstract: One of the first beings affected by changes in the climate are trees, one of our most vital resources. In this study tree species interaction and the response to climate in different ecological environments is observed by applying a joint species distribution model to different ecological domains in the United States. Joint species distribution models are useful to learn inter-species relationships and species response to the environment. The climates’ impact on the tree species is measured through species abundance in an area. We compare the model’s performance across all ecological domains and study the sensitivity of the climate variables. With the prediction of abundances, tree species populations can be predicted in the future and measure the impact of climate change on tree populations. Authors: Hyun Choi (University of Florida); Sergio Marconi (University of Florida); Ali Sadeghian (University of Florida); Ethan White (University of Florida); Daisy Zhe Wang (Univeresity of Florida)	Land Use Climate Science & Modeling Forests
NeurIPS 2019	A Global Census of Solar Facilities Using Deep Learning and Remote Sensing (Papers Track) Honorable Mention Abstract and authors: (click to expand) Abstract: We present a comprehensive global census of solar power facilities using deep learning and remote sensing. We search imagery from the Airbus SPOT 6/7 and European Space Agency Sentinel-2 satellites covering more than 48% of earth’s land-surface using a combination of deep-learning models, image processing, and hand-verification. We locate solar facilities and measure their footprints and installation dates. The resulting dataset of 68,797 facilities has an estimated generating capacity of 209 GW; 78% of this capacity was not previously reported in public databases. These asset-level data are critical for understanding energy infrastructure, evaluate climate risk, and efficiently use intermittent solar energy - ultimately enabling the transition to a predominantly renewable energy system. Authors: Lucas Kruitwagen (University of Oxford); Kyle Story (Descartes Labs); Johannes Friedrich (World Resource Institute); Sam Skillman (Descartes Labs); Cameron Hepburn (University of Oxford)	Power & Energy
NeurIPS 2019	Machine Learning for Precipitation Nowcasting from Radar Images (Papers Track) Abstract and authors: (click to expand) Abstract: High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dramatic promise in many domains, including the geosciences, we present an application of DL to the problem of precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1 hour) predictions of precipitation. We treat forecasting as an image-to-image translation problem and leverage the power of the ubiquitous UNET convolutional neural network. We find this performs favorably when compared to three commonly used models: optical flow, persistence and NOAA's numerical one-hour HRRR nowcasting prediction. Authors: Shreya Agrawal (Google); Luke Barrington (Google); Carla Bromberg (Google); John Burge (Google); Cenk Gazen (Google); Jason Hickey (Google)	Climate Science & Modeling Extreme Weather
NeurIPS 2019	Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon (Papers Track) Abstract and authors: (click to expand) Abstract: The ability to analyze and forecast stratospheric weather conditions is fundamental to addressing climate change. However, our capacity to collect data in the stratosphere is limited by sparsely deployed weather balloons. We propose a framework to collect stratospheric data by releasing a contrail of tiny sensor devices as a weather balloon ascends. The key machine learning challenges are determining when and how to deploy a finite collection of sensors to produce a useful data set. We decide when to release sensors by modeling the deviation of a forecast from actual stratospheric conditions as a Gaussian process. We then implement a novel hardware system that is capable of optimally releasing sensors from a rising weather balloon. We show that this data engineering framework is effective through real weather balloon flights, as well as simulations. Authors: Kiwan Maeng (Carnegie Mellon University); Iskender Kushan (Microsoft); Brandon Lucia (Carnegie Mellon University); Ashish Kapoor (Microsoft)	Climate Science & Modeling
NeurIPS 2019	Automatic data cleaning via tensor factorization for large urban environmental sensor networks (Papers Track) Abstract and authors: (click to expand) Abstract: The US Environmental Protection Agency identifies that urban heat islands can negatively impact a community’s environment and quality of life. Using low cost urban sensing networks, it is possible to measure the impacts of mitigation strategies at a fine-grained scale, informing context-aware policies and infrastructure design. However, fine-grained city-scale data analysis is complicated by tedious data cleaning including removing outliers and imputing missing data. To address the challenge of data cleaning, this article introduces a robust low-rank tensor factorization method to automatically correct anomalies and impute missing entries for high-dimensional urban environmental datasets. We validate the method on a synthetically degraded National Oceanic and Atmospheric Administration temperature dataset, with a recovery error of 4%, and apply it to the Array of Things city-scale sensor network in Chicago, IL. Authors: Yue Hu (Vanderbilt University); Yanbing Wang (Vanderbilt University); Canwen Jiao (Vanderbilt University); Rajesh Sankaran (Argonne National Lab); Charles Catlett (Argonne National Lab); Daniel Work (Vanderbilt University)	Buildings
NeurIPS 2019	Identify Solar Panels in Low Resolution Satellite Imagery with Siamese Architecture and Cross-Correlation (Papers Track) Abstract and authors: (click to expand) Abstract: Understanding solar adoption trends and their underlying dynamics requires a comprehensive and granular time-series solar installation database which is unavailable today and expensive to create manually. To this end, we leverage a deep siamese network that automatically identifies solar panels in historical low-resolution (LR) satellite images by comparing the target image with its high-resolution exemplar at the same location. To resolve the potential displacement between solar panels in the exemplar image and that in the target image, we use a cross-correlation module to collate the spatial features learned from each input and measure their similarity. Experimental result shows that our model significantly outperforms baseline methods on a dataset of historical LR images collected in California. Authors: Zhengcheng Wang (Tsinghua University); Zhecheng Wang (Stanford University); Arun Majumdar (Stanford University); Ram Rajagopal (Stanford University)	Power & Energy
NeurIPS 2019	VideoGasNet: Deep Learning for Natural Gas Methane Leak Classification Using An Infrared Camera (Papers Track) Abstract and authors: (click to expand) Abstract: Mitigating methane leakage from the natural gas system have become an increasing concern for climate change. Efficacious methane leak detection and classification can make the mitigation process more efficient and cost effective. Optical gas imaging is widely used for the purpose of leak detection, but it cannot directly provide detection results and leak sizes. Few studies have examined the possibility of leak classification using videos taken by the infrared camera (IR), an optical gas imaging device. In this study, we consider the leak classification problem as a video classification problem and investigated the application of deep learning techniques in methane leak detection. Firstly we collected the first methane leak video dataset - GasVid, which has ~1 M frames of labeled videos of methane leaks from different leaking equipment, covering a wide range of leak sizes (5.3-2051.6 g\ce{CH4}/h) and imaging distances (4.6-15.6 m). Secondly, we studied three deep learning algorithms, including 2D Convolutional Neural Networks (CNN) model, 3D CNN and the Convolutional Long Short Term Memory (ConvLSTM). We find that 3D CNN is the most outstanding and robust architecture, which was named VideoGasNet. The leak-non-leak detection accuracy can reach 100%, and the highest small-medium-large classification accuracy is 78.2% with our 3D CNN network. In summary, VideoGasNet greatly extends the capabilities of IR camera-based leak monitoring system from leak detection only to automated leak classification with high accuracy and fast processing speed, significant mitigation efficiency. Authors: Jingfan Wang (Stanford University)	Climate Science & Modeling Ecosystems & Biodiversity
NeurIPS 2019	Detecting Avalanche Deposits using Variational Autoencoder on Sentinel-1 Satellite Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Avalanche monitoring is a crucial safety challenge, especially in a changing climate. Remote sensing of avalanche deposits can be very useful to identify avalanche risk zones and time periods, which can in turn provide insights about the effects of climate change. In this work, we use Sentinel-1 SAR (synthetic aperture radar) data on the French Alps for the exceptional winter of 2017-18, with the goal of automatically detecting avalanche deposits. We address our problem with an unsupervised learning technique. We treat an avalanche as a rare event, or an anomaly, and we learn a variational autoencoder, in order to isolate the anomaly. We then evaluate our method on labeled test data, using an independent in-situ avalanche inventory as ground truth. Our empirical results show that our unsupervised method obtains comparable performance to a recent supervised learning approach that trained a convolutional neural network on an artificially balanced version of the same SAR data set along with the corresponding ground-truth labels. Our unsupervised approach outperforms the standard CNN in terms of balanced accuracy (63% as compared to 55%). This is a significant improvement, as it allows our method to be used in-situ by climate scientists, where the data is always very unbalanced (< 2% positives). This is the first application of unsupervised deep learning to detect avalanche deposits. Authors: Saumya Sinha (University of Colorado, Boulder); Sophie Giffard-Roisin (University of Colorado Boulder); Fatima Karbou (Meteo France); Michael Deschatres (Irstea); Nicolas Eckert (Irstea); Anna Karas (Meteo France); Cécile Coléou (Meteo France); Claire Monteleoni (University of Colorado Boulder)	Extreme Weather Climate Science & Modeling
NeurIPS 2019	Fine-Grained Distribution Grid Mapping Using Street View Imagery (Papers Track) Abstract and authors: (click to expand) Abstract: Fine-grained distribution grid mapping is essential for power system operation and planning in the aspects of renewable energy integration, vegetation management, and risk assessment. However, currently such information can be inaccurate, outdated, or incomplete. Existing grid topology reconstruction methods heavily rely on various assumptions and measurement data that is not widely available. To bridge this gap, we propose a machine-learning-based method that automatically detects, localizes, and estimates the interconnection of distribution power lines and utility poles using readily-available street views in the upward perspective. We demonstrate the superior image-level and region-level accuracy of our method on a real-world distribution grid test case. Authors: Qinghu Tang (Tsinghua University); Zhecheng Wang (Stanford University); Arun Majumdar (Stanford University); Ram Rajagopal (Stanford University)	Power & Energy
NeurIPS 2019	Bayesian optimization with theory-based constraints accelerates search for stable photovoltaic perovskite materials (Papers Track) Abstract and authors: (click to expand) Abstract: Bringing a new photovoltaic technology from materials research stage to the market has historically taken decades, and the process has to be accelerated for increasing the share of renewables in energy production. We demonstrate Bayesian optimization for accelerating stability research. Convergence is reached even faster when using a constraint for integrating physical knowledge into the model. In our test case, we optimize the stability of perovskite compositions for perovskite solar cells, an efficient new solar cell technology suffering from limited lifetime of devices. Authors: Armi Tiihonen (Massachusetts Institute of Technology)	Power & Energy
NeurIPS 2019	Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning (Papers Track) Abstract and authors: (click to expand) Abstract: New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV). This paper explores the feasibility of using deep reinforcement learning (DRL) to optimize a driving and charging policy for a ride-hailing EV agent, with the goal of reducing costs and emissions while increasing transportation service provided. We introduce a data-driven simulation of a ride-hailing EV agent that provides transportation service and charges energy at congested charging infrastructure. We then formulate a test case for the sequential driving and charging decision making problem of the agent and apply DRL to optimize the agent's decision making policy. We evaluate the performance against heuristic policies and show that our agent learns to act competitively without any prior knowledge. Authors: Jon Donadee (LLNL); Jacob Pettit (LLNL); Ruben Glatt (LLNL); Brenden Petersen (Lawrence Livermore National Laboratory)	Transportation Power & Energy
NeurIPS 2019	Stripping off the implementation complexity of physics-based model predictive control for buildings via deep learning (Papers Track) Abstract and authors: (click to expand) Abstract: Over the past decade, model predictive control (MPC) has been considered as the most promising solution for intelligent building operation. Despite extensive effort, transfer of this technology into practice is hampered by the need to obtain an accurate controller model with minimum effort, the need of expert knowledge to set it up, and the need of increased computational power and dedicated software to run it. A promising direction that tackles the last two problems was proposed by approximate explicit MPC where the optimal control policies are learned from MPC data via a suitable function approximator, e.g., a deep learning (DL) model. The main advantage of the proposed approach stems from simple evaluation at execution time leading to low computational footprints and easy deployment on embedded HW platforms. We present the energy savings potential of physics-based (also called 'white-box') MPC applied to an office building in Belgium. Moreover, we demonstrate how deep learning approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the major challenges and remaining open-research questions. Authors: Jan Drgona (Pacific Northwest National Laboratory); Lieve Helsen (KU Leuven); Draguna Vrabie (PNNL)	Buildings Reinforcement Learning
NeurIPS 2019	Machine learning identifies the most valuable synthesis conditions for next-generation photovoltaics (Papers Track) Best Paper Award Abstract and authors: (click to expand) Abstract: Terawatts of next-generation photovoltaics (PV) are necessary to mitigate climate change. The traditional R&D paradigm leads to high efficiency / high variability solar cells, limiting industrial scaling of novel PV materials. In this work, we propose a machine learning approach for early-stage optimization of solar cells, by combining a physics-informed deep autoencoder and a manufacturing-relevant Bayesian optimization objective. This framework allows to: 1) Co-optimize solar cell performance and variability under techno-economic revenue constrains, and 2) Infer the effect of process conditions over key latent physical properties. We test our approach by synthesizing 135 perovskite solar cells, and finding the optimal points under various techno-economic assumptions. Authors: Felipe Oviedo (MIT) and Zekun Ren (MIT)	Power & Energy Heavy Industry and Manufacturing
NeurIPS 2019	Helping Reduce Environmental Impact of Aviation with Machine Learning (Papers Track) Best Paper Award Abstract and authors: (click to expand) Abstract: Commercial aviation is one of the biggest contributors towards climate change. We propose to reduce environmental impact of aviation by considering solutions that would reduce the flight time. Specifically, we first consider improving winds aloft forecast so that flight planners could use better information to find routes that are efficient. Secondly, we propose an aircraft routing method that seeks to find the fastest route to the destination by considering uncertainty in the wind forecasts and then optimally trading-off between exploration and exploitation. Both these ideas were previously published in [5] and [8] and contain further technical details. Authors: Ashish Kapoor (Microsoft)	Transportation
NeurIPS 2019	Machine Learning for Generalizable Prediction of Flood Susceptibility (Papers Track) Abstract and authors: (click to expand) Abstract: Flooding is a destructive and dangerous hazard and climate change appears to be increasing the frequency of catastrophic flooding events around the world. Physics-based flood models are costly to calibrate and are rarely generalizable across different river basins, as model outputs are sensitive to site-specific parameters and human-regulated infrastructure. Statistical models trained primarily from remotely-sensed Earth observation data could reduce the need for extensive in-situ measurements. In this work, we develop generalizable, multi-basin models of river flooding susceptibility using geographically-distributed data from the USGS stream gauge network. Machine learning models are trained in a supervised framework to predict two measures of flood susceptibility from a mix of river basin attributes, impervious surface cover information derived from satellite imagery, and historical records of rainfall and stream height. We report prediction performance of multiple models using precision-recall curves, and compare with performance of naive baselines. This work on multi-basin flood prediction represents a step in the direction of making flood prediction accessible to all at-risk communities. Authors: Dylan Fitzpatrick (Carnegie Mellon University); Chelsea Sidrane (Stanford University); Andrew Annex (Johns Hopkins University); Diane O'Donoghue (kx); Piotr Bilinski (University of Warsaw)	Disaster Management and Relief Extreme Weather
NeurIPS 2019	A Deep Learning-based Framework for the Detection of Schools of Herring in Echograms (Papers Track) Abstract and authors: (click to expand) Abstract: Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm that uses hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries. Authors: Alireza Rezvanifar (University of Victoria); Tunai Porto Marques (University of Victoria ); Melissa Cote (University of Victoria); Alexandra Branzan Albu (University of Victoria); Alex Slonimer (ASL Environmental Sciences); Thomas Tolhurst (ASL Environmental Sciences ); Kaan Ersahin (ASL Environmental Sciences ); Todd Mudge (ASL Environmental Sciences ); Stephane Gauthier (Fisheries and Oceans Canada)	Ecosystems & Biodiversity Climate Science & Modeling
NeurIPS 2019	Emulating Numeric Hydroclimate Models with Physics-Informed cGANs (Papers Track) Honorable Mention Abstract and authors: (click to expand) Abstract: Process-based numerical simulations, including those for climate modeling applications, are compute and resource intensive, requiring extensive customization and hand-engineering for encoding governing equations and other domain knowledge. On the other hand, modern deep learning employs a significantly simpler and more efficient computational workflow, and has been shown impressive results across a myriad of applications in the computational sciences. In this work, we investigate the potential of deep generative learning models, specifically conditional Generative Adversarial Networks (cGANs), to simulate the output of a physics-based model of the spatial distribution of the water content of mountain snowpack - the snow water equivalent (SWE). We show preliminary results indicating that the cGAN model is able to learn diverse mappings between meteorological forcings and SWE output. Thus physics based cGANs provide a means for fast and accurate SWE modeling that can have significant impact in a variety of applications (e.g., hydropower forecasting, agriculture, and water supply management). In climate science, the Snowpack and SWE are seen as some of the best indicative variables for investigating climate change and its impact. The massive speedups, diverse sampling, and sensitivity/saliency modelling that cGANs can bring to SWE estimation will be extremely important to investigating variables linked to climate change as well as predicting and forecasting the potential effects of climate change to come. Authors: Ashray Manepalli (terrafuse); Adrian Albert (terrafuse, inc.); Alan Rhoades (Lawrence Berkeley National Lab); Daniel Feldman (Lawrence Berkeley National Lab)	Ecosystems & Biodiversity Climate Science & Modeling
NeurIPS 2019	Forecasting El Niño with Convolutional and Recurrent Neural Networks (Papers Track) Abstract and authors: (click to expand) Abstract: The El Niño Southern Oscillation (ENSO) is the dominant mode of variability in the climate system on seasonal to decadal timescales. With foreknowledge of the state of ENSO, stakeholders can anticipate and mitigate impacts in climate-sensitive sectors such as agriculture and energy. Traditionally, ENSO forecasts have been produced using either computationally intensive physics-based dynamical models or statistical models that make limiting assumptions, such as linearity between predictors and predictands. Here we present a deep-learning-based methodology for forecasting monthly ENSO temperatures at various lead times. While traditional statistical methods both train and validate on observational data, our method trains exclusively on physical simulations. With the entire observational record as an out-of-sample validation set, the method’s skill is comparable to that of operational dynamical models. The method is also used to identify disagreements among climate models about the predictability of ENSO in a world with climate change. Authors: Ankur Mahesh (ClimateAi); Maximilian Evans (ClimateAi); Garima Jain (ClimateAi); Mattias Castillo (ClimateAi); Aranildo Lima (ClimateAi); Brent Lunghino (ClimateAi); Himanshu Gupta (ClimateAi); Carlos Gaitan (ClimateAi); Jarrett Hunt (ClimateAi); Omeed Tavasoli (ClimateAi); Patrick Brown (ClimateAi, San Jose State University); V. Balaji (Geophysical Fluid Dynamics Laboratory)	Climate Science & Modeling Forecasting
NeurIPS 2019	Deep learning predictions of sand dune migration (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate change is making many desert regions warmer, drier, and sandier. These conditions kill vegetation, and release once-stable sand into the wind, allowing it to form dunes that threaten roads, farmland, and solar panel installations. With enough warning, people can mitigate dune damages by moving infrastructure or restoring vegetation. Current dune simulations, however, do not scale well enough to provide useful forecasts for the ~5% of Earth's land surface that is covered by mobile sands. We propose to train a deep learning simulation to emulate the output of a community-standard physics-based dune simulation. We will base the new model on a GAN-based video prediction model with an excellent track record for predicting spatio-temporal patterns to model, and use it to simulate dune topographies over time. Our preliminary work indicates that the new model will run up to ten million times faster than existing dune simulations, which would turn dune modelling from an exercise that covers a handful of dunes to a practical forecast for large desert regions. Authors: Kelly Kochanski (University of Colorado Boulder); Divya Mohan (University of California Berkeley); Jenna Horrall (James Madison University); Ghaleb Abdulla (Lawrence Livermore National Laboratory)	Climate Science & Modeling Extreme Weather
NeurIPS 2019	Predictive Inference of a Wildfire Risk Pipeline in the United States (Proposals Track) Abstract and authors: (click to expand) Abstract: Wildfires are rare events that present severe threats to life and property. Understanding their propagation is of key importance to mitigate and contain their impact, especially since climate change is increasing their occurrence. We propose an end-to-end sequential model of wildfire risk components, including wildfire location, size, duration, and risk exposure. We do so through a combination of marked spatio-temporal point processes and conditional density estimation techniques. Unlike other approaches that use regression-based methods, this approach allows both predictive accuracy and an associated uncertainty measure for each risk estimate, accounting for the uncertainty in prior model components. This is particularly beneficial for timely decision-making by different wildfire risk management stakeholders. To allow us to build our models without limiting them to a specific state or county, we have collected open wildfire and climate data for the entire continental United States. We are releasing this aggregated dataset to enable further o pen research on wildfire models at a national scale. Authors: Shamindra Shrotriya (Carnegie Mellon University); Niccolo Dalmasso (Carnegie Mellon University); Alex Reinhart (Carnegie Mellon University)	Extreme Weather Disaster Management and Relief
NeurIPS 2019	FutureArctic - beyond Computational Ecology (Proposals Track) Abstract and authors: (click to expand) Abstract: This paper presents the Future Arctic initiative, a multi-disciplinary training network where machine learning researchers and ecologists cooperatively study both long- and short-term responses to future climate in Iceland. Authors: Steven Latre (UAntwerpen); Dimitri Papadimitriou (UAntwerpen); Ivan Janssens (UAntwerpen); Eric Struyf (UAntwerpen); Erik Verbruggen (UAntwerpen); Ivika Ostonen (UT); Josep Penuelas (UAB); Boris Rewald (RootEcology); Andreas Richter (University of Vienna); Michael Bahn (University of Innsbruck)	Climate Science & Modeling Ecosystems & Biodiversity
NeurIPS 2019	Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts (Proposals Track) Abstract and authors: (click to expand) Abstract: An increasing amount of companies and cities plan to become CO2-neutral, which requires them to invest in renewable energies and carbon emission offsetting solutions. One of the cheapest carbon offsetting solutions is preventing deforestation in developing nations, a major contributor in global greenhouse gas emissions. However, forest preservation projects historically display an issue of trust and transparency, which drives companies to invest in transparent, but expensive air carbon capture facilities. Preservation projects could conduct accurate forest inventories (tree diameter, species, height etc.) to transparently estimate the biomass and amount of stored carbon. However, current rainforest inventories are too inaccurate, because they are often based on a few expensive ground-based samples and/or low-resolution satellite imagery. LiDAR-based solutions, used in US forests, are accurate, but cost-prohibitive, and hardly-accessible in the Amazon rainforest. We propose accurate and cheap forest inventory analyses through Deep Learning-based processing of drone imagery. The more transparent estimation of stored carbon will create higher transparency towards clients and thereby increase trust and investment into forest preservation projects. Authors: Björn Lütjens (MIT); Lucas Liebenwein (Massachusetts Institute of Technology); Katharina Kramer (Massachusetts Institute of Technology)	Carbon Capture & Sequestration Forests
NeurIPS 2019	DeepRI: End-to-end Prediction of Tropical Cyclone Rapid Intensification from Climate Data (Proposals Track) Abstract and authors: (click to expand) Abstract: Predicting rapid intensification (RI) is extremely critical in tropical cyclone forecasting. Existing deep learning models achieve promising results, however still rely on hand-craft feature. We propose to design an end-to-end deep learning architecture that directly predict RI from raw climate data without intermediate heuristic feature, which allows joint optimization of the whole system for higher performance. Authors: Renzhi Jing (Princeton University); Ning Lin (Princeton University); Yinda Zhang (Google LLC)	Extreme Weather Climate Science & Modeling
NeurIPS 2019	Autonomous Sensing and Scientific Machine Learning for Monitoring Greenhouse Gas Emissions (Proposals Track) Abstract and authors: (click to expand) Abstract: Greenhouse gas emissions are a key driver of climate change. In order to develop and tune climate models, measurements of natural and anthropogenic phenomenon are necessary. Traditional methods (i.e., physical sample collection and ex situ analysis) tend to be sample sparse and low resolution, whereas global remote sensing methods tend to miss small- and mid-scale dynamic phenomenon. In situ instrumentation carried by a robotic platform is suited to study greenhouse gas emissions at unprecedented spatial and temporal resolution. However, collecting scientifically rich datasets of dynamic or transient emission events requires accurate and flexible models of gas emission dynamics. Motivated by applications in seasonal Arctic thawing and volcanic outgassing, we propose the use of scientific machine learning, in which traditional scientific models (in the form of ODEs/PDEs) are combined with machine learning techniques (generally neural networks) to better incorporate data into a structured, interpretable model. Our technical contributions will primarily involve developing these hybrid models and leveraging model uncertainty estimates during sensor planning to collect data that efficiently improves gas emission models in small-data domains. Authors: Genevieve Flaspohler (MIT); Victoria Preston (MIT); Nicholas Roy (MIT); John Fisher (MIT); Adam Soule (Woods Hole Oceanographic Institution); Anna Michel (Woods Hole Oceanographic Institution)	Climate Science & Modeling Ecosystems & Biodiversity
NeurIPS 2019	Optimizing trees for carbon sequestration (Proposals Track) Abstract and authors: (click to expand) Abstract: In the IPCC models of climate change mitigation, most scenarios ensuring less than 2ºC of warming assume deployment of some form of “negative emissions technology,” alongside dramatic reductions in emissions and other major societal changes. Proposed negative emissions technologies include bioenergy with carbon capture and storage, enhanced weathering of minerals, direct air capture, and afforestation / reforestation. Among these technologies, the use of trees for carbon sequestration through photosynthesis is well established, requires little energy, has comparable sequestration potential, and can be deployed at scale for relatively low cost. The primary constraint on using trees for sequestration is land, which is limited and increasingly subject to competitive demand. Thus, maximizing the capacity and long-term stability of every hectare used for planting would bolster the critical role of trees in a broad negative emissions strategy. Here, we propose to build a new data resource and optimization tool that leverages modern measurements and machine learning to help address this need. Authors: Jeremy Freeman	Carbon Capture & Sequestration Forests
NeurIPS 2019	Toward Resilient Cities: Using Deep Learning to Downscale Climate Model Projections (Proposals Track) Abstract and authors: (click to expand) Abstract: Climate projections from Earth System Models (ESM) are widely used to assess climate change impacts. These projections, however, are too coarse in spatial and temporal resolution (e.g. 25-50 kms, monthly) to be used in local scale resilience studies. High-resolution (<4 km) climate projections at dense temporal resolution (hourly) from multiple Earth System models under various scenarios are necessary to assess potential future changes in climate variables and perform meaningful and robust climate resilience studies. Running ESMs in high-resolution is computationally too expensive, therefore downscaling methods are applied to ESM projections to produce high-resolution projections. Using a regional climate model to downscale climate projections is preferred but dynamically downscaling several ESM projections to < 4km resolution under different scenarios is currently not feasible. In this study, we propose to use a 60 year dynamically downscaled climate dataset with hourly output for the Northeastern United States to train Deep Learning models and achieve a computationally efficient method of downscaling climate projections. This method will allow for more ESM projections to be downscaled to local scales under more scenarios in an efficient manner and significantly improve robustness of regional resilience studies. Authors: Muge Komurcu (MIT); Zikri Bayraktar (IEEE)	Climate Science & Modeling Extreme Weather
NeurIPS 2019	Towards self-adaptive building energy control in smart grids (Proposals Track) Abstract and authors: (click to expand) Abstract: Energy consumption in buildings greatly contributes to worldwide CO2 emissions and thus any improvement in HVAC operation will greatly help tackling global climate change. We are putting forward a proposal for self-adaptive energy control in smart grids based on Deep Learning, Deep Reinforcement Learning and Multi-Agent technologies. Particularly, we introduce the concept of Deep Neural Simulation Model (DNSM) as a way of generating digital twins of buildings in which the agent can test and learn optimal operations by itself and by collaborating with other agents. Not only do we expect a reduction on energy consumption and an increment on the use of renewable sources, but also a reduction on the cost of controlling energy in buildings. Authors: Juan Gómez-Romero (Universidad de Granada); Miguel Molina-Solana (Imperial College London)	Buildings Power & Energy
NeurIPS 2019	Predicting Arctic Methane Seeps via Satellite Imagery (Proposals Track) Abstract and authors: (click to expand) Abstract: The arctic has seen significant warming and releases of methane, a potent greenhouse gas have been reported. We aim to apply computer vision to satellite imagery in order to quantify geological methane emissions from the permafrost as well as track and predict their change due to increasing temperatures. Authors: Olya (Olga) Irzak (Frost Methane Labs); Amber Leigh Thomas (Stanford); Stephanie Schneider (Stanford); Catalin Voss (Stanford University)	Climate Science & Modeling
NeurIPS 2019	GeoLabels: Towards Efficient Ecosystem Monitoring using Data Programming on Geospatial Information (Proposals Track) Abstract and authors: (click to expand) Abstract: Monitoring, Reporting and Verification (MRV) systems for land use play a key role in the decision-making of climate investors, policymakers and conservationists. Remote sensing is commonly used for MRV but practical solutions are constrained by a lack of labels to train machine learning-based downstream tasks. GeoLabels is an automated MRV system that can rapidly adapt to novel applications by leveraging existing geospatial information and domain expertise to quickly create training sets through data programming. Moreover, GeoLabels uses dimensionality reduction interfaces, allowing non-technical users to create visual labeling functions. Authors: David Dao (ETH); Johannes Rausch (ETH Zurich); Ce Zhang (ETH)
NeurIPS 2019	A deep learning approach for classifying black carbon aerosol morphology (Proposals Track) Abstract and authors: (click to expand) Abstract: Black carbon (BC) is a sub-micron aerosol sourced from incomplete combustion which strongly absorbs solar radiation, leading to both direct and indirect climate impacts. The state-of-the-art technique for characterizing BC is the single particle soot photometer (SP2) instrument, which detects these aerosols in real time via laser-induced incandescence (L-II). This measurement technique allows for quantification of BC mass on a single particle basis, but time-resolved signals may also provide constraints on BC morphology, which impacts both its optical properties and atmospheric lifetime. No methods currently exist to use this information. I propose applying a deep learning based approach to classify the fractal dimension of single BC particles from time-resolved L-II signals. This method would provide the first on-line measurement technique for quantifying BC morphology. These observations could be used to improve representations of BC optical properties and atmospheric processing in climate models. Authors: Kara Lamb (Cooperative Institute for Research in the Environmental Sciences)	Climate Science & Modeling
ICML 2019	Policy Search with Non-uniform State Representations for Environmental Sampling (Research Track) Abstract and authors: (click to expand) Abstract: Surveying fragile ecosystems like coral reefs is important to monitor the effects of climate change. We present an adaptive sampling technique that generates efficient trajectories covering hotspots in the region of interest at a high rate. A key feature of our sampling algorithm is the ability to generate action plans for any new hotspot distribution using the parameters learned on other similar looking distributions. Authors: Sandeep Manjanna (McGill University); Herke van Hoof (University of Amsterdam); Gregory Dudek (McGill University)	Ecosystems & Biodiversity
ICML 2019	Modelling GxE with historical weather information improves genomic prediction in new environments (Research Track) Abstract and authors: (click to expand) Abstract: Interaction between the genotype and the environment ($G \times E$) has a strong impact on the yield of major crop plants. Recently $G \times E$ has been predicted from environmental and genomic covariates, but existing works have not considered generalization to new environments and years without access to in-season data. We study \textit{in silico} the viability of $G \times E$ prediction under realistic constraints. We show that the environmental response of a new generation of untested Barley cultivars can be predicted in new locations and years using genomic data, machine learning and historical weather observations. Our results highlight the need for models of $G \times E$: non-linear effects clearly dominate linear ones and the interaction between the soil type and daily rain is identified as the main driver for $G \times E$. Our study implies that genomic selection can be used to capture the yield potential in $G \times E$ effects for future growth seasons, providing a possible means to achieve yield improvements. $G \times E$ models are also needed to select for varieties that react favourably to the altering climate conditions. For this purpose, the historical weather observations could be replaced by climate simulations to study the yield potential under various climate scenarios.This abstract summarizes the findings of a recently published article. Authors: Jussi Gillberg (Aalto University); Pekka Marttinen (Aalto University); Hiroshi Mamitsuka (Kyoto University); Samuel Kaski (Aalto University)	Agriculture & Food
ICML 2019	Machine Learning empowered Occupancy Sensing for Smart Buildings (Research Track) Abstract and authors: (click to expand) Abstract: Over half of the global electricity consumption is attributed to buildings, which are often operated poorly from an energy perspective. Significant improvements in energy efficiency can be achieved via intelligent building control techniques. To realize such advanced control schemes, accurate and robust occupancy information is highly valuable. In this work, we present a cutting-edge WiFi sensing platform and state-of-the-art machine learning methods to address longstanding occupancy sensing challenges in smart buildings. Our systematic solution provides comprehensive fine-grained occupancy information in a non-intrusive and privacy-preserving manner, which facilitates eco-friendly and sustainable buildings. Authors: Han Zou (UC Berkeley); Hari Prasanna Das (UC Berkeley ); Jianfei Yang (Nanyang Technological University); Yuxun Zhou (UC Berkeley); Costas Spanos (UC Berkeley)	Buildings
ICML 2019	Focus and track: pixel-wise spatio-temporal hurricane tracking (Research Track) Abstract and authors: (click to expand) Abstract: We tackle extreme climate event tracking problem. It has unique challenges to other visual object tracking problems, including wider range of spatio-temporal dynamics, blur boundary of the target, and shortage of labeled dataset. In this paper, we propose a simple but robust end-to-end model based on multi-layered ConvLSTM, suitable for the climate event tracking problem. It first learns to imprint location and appearance of the target at the first frame with one-shot auto-encoding fashion, and then, the learned feature is consumed by the tracking module to track the target in subsequent time frames. To tackle the data shortage problem, we propose data augmentation based on Social GAN. Extensive experiments show that the proposed framework significantly improves tracking performance on hurricane tracking task over several state-of-the-art methods. Authors: Sookyung Kim (Lawrence Livermore National Laboratory); Sunghyun Park (Korea University); Sunghyo Chung (Korea University); Yunsung Lee (Korea University); Hyojin Kim (LLNL); Joonseok Lee (Google Research); Jaegul Choo (Korea University); Mr Prabhat (Lawrence Berkeley National Laboratory)	Climate Science & Modeling Extreme Weather
ICML 2019	Recovering the parameters underlying the Lorenz-96 chaotic dynamics (Research Track) Abstract and authors: (click to expand) Abstract: Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to resolve key processes such as clouds and convection. These processes are approximated using heuristics in a process called parameterization. The selection of these parameters can be subjective, leading to significant uncertainties in the way clouds are represented in global climate models. Here, we explore three deep network algorithms to infer these parameters in an objective and data-driven way. We compare the performance of a fully-connected network, a one-dimensional and, a two-dimensional convolutional networks to recover the underlying parameters of the Lorenz-96 model, a non-linear dynamical system that has similar behavior to the climate system. Authors: Soukayna Mouatadid (University of Toronto); Pierre Gentine (Columbia University); Wei Yu (University of Toronto); Steve Easterbrook (University of Toronto)	Climate Science & Modeling Extreme Weather
ICML 2019	Using Bayesian Optimization to Improve Solar Panel Performance by Developing Antireflective, Superomniphobic Glass (Research Track) Abstract and authors: (click to expand) Abstract: Photovoltaic solar panel efficiency is dependent on photons transmitting through the glass sheet covering and into the crystalline silicon solar cells within. However, complications such as soiling and light reflection degrade performance. Our goal is to identify a fabrication process to produce glass which promotes photon transmission and is superomniphobic (repels fluids), for easier cleaning. In this paper, we propose adapting Bayesian optimization to efficiently search the space of possible glass fabrication strategies; in this search we balance three competing objectives (transmittance, haze and oil contact angle). We present the glass generated from this Bayesian optimization strategy and detail its properties relevant to photovoltaic solar power. Authors: Sajad Haghanifar (University of Pittsburgh); Bolong Cheng (SigOpt); Mike Mccourt (SigOpt); Paul Leu (University of Pittsburgh)	Power & Energy
ICML 2019	A quantum mechanical approach for data assimilation in climate dynamics (Research Track) Abstract and authors: (click to expand) Abstract: A framework for data assimilation in climate dynamics is presented, combining aspects of quantum mechanics, Koopman operator theory, and kernel methods for machine learning. This approach adapts the Dirac-von Neumann formalism of quantum dynamics and measurement to perform data assimilation (filtering) of climate dynamics, using the Koopman operator governing the evolution of observables as an analog of the Heisenberg operator in quantum mechanics, and a quantum mechanical density operator to represent the data assimilation state. The framework is implemented in a fully empirical, data-driven manner, using kernel methods for machine learning to represent the evolution and measurement operators via matrices in a basis learned from time-ordered observations. Applications to data assimilation of the Nino 3.4 index for the El Nino Southern Oscillation (ENSO) in a comprehensive climate model show promising results. Authors: Dimitrios Giannakis (Courant Institute of Mathematical Sciences, New York University); Joanna Slawinska (University of Wisconsin-Milwaukee); Abbas Ourmazd (University of Wisconsin-Milwaukee)	Climate Science & Modeling
ICML 2019	Data-driven Chance Constrained Programming based Electric Vehicle Penetration Analysis (Research Track) Abstract and authors: (click to expand) Abstract: Transportation electrification has been growing rapidly in recent years. The adoption of electric vehicles (EVs) could help to release the dependency on oil and reduce greenhouse gas emission. However, the increasing EV adoption will also impose a high demand on the power grid and may jeopardize the grid network infrastructures. For certain high EV penetration areas, the EV charging demand may lead to transformer overloading at peak hours which makes the maximal EV penetration analysis an urgent problem to solve. This paper proposes a data-driven chance constrained programming based framework for maximal EV penetration analysis. Simulation results are presented for a real-world neighborhood level network. The proposed framework could serve as a guidance for utility companies to schedule infrastructure upgrades. Authors: Di Wu (McGill); Tracy Cui (Google NYC); Doina Precup (McGill University); Benoit Boulet (McGill)	Power & Energy
ICML 2019	Machine Learning for AC Optimal Power Flow (Research Track) Honorable Mention Abstract and authors: (click to expand) Abstract: F( We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids. Authors: Neel Guha (Carnegie Mellon University); Zhecheng Wang (Stanford University); Arun Majumdar (Stanford University)	Power & Energy Heavy Industry and Manufacturing
ICML 2019	Targeted Meta-Learning for Critical Incident Detection in Weather Data (Research Track) Abstract and authors: (click to expand) Abstract: Due to imbalanced or heavy-tailed nature of weather- and climate-related datasets, the performance of standard deep learning models significantly deviates from their expected behavior on test data. Classical methods to address these issues are mostly data or application dependent, hence burdensome to tune. Meta-learning approaches, on the other hand, aim to learn hyperparameters in the learning process using different objective functions on training and validation data. However, these methods suffer from high computational complexity and are not scalable to large datasets. In this paper, we aim to apply a novel framework named as targeted meta-learning to rectify this issue, and show its efficacy in dealing with the aforementioned biases in datasets. This framework employs a small, well-crafted target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner. We empirically show that this framework can overcome the bias issue, common to weather-related datasets, in a bow echo detection case study. Authors: Mohammad Mahdi Kamani (The Pennsylvania State University); Sadegh Farhang (Pennsylvania State University); Mehrdad Mahdavi (Penn State); James Z Wang (The Pennsylvania State University)	Extreme Weather Climate Science & Modeling
ICML 2019	ClimateNet: Bringing the power of Deep Learning to weather and climate sciences via open datasets and architectures (Research Track) Abstract and authors: (click to expand) Abstract: Pattern recognition tasks such as classification, object detection and segmentation have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting weather patterns and extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of Deep Learning in tackling similar problems in computer vision, we advocate a DL-based approach. However, DL works best in the context of supervised learning, when labeled datasets are readily available. Reliable, labeled training data is scarce in climate science. `ClimateNet' is an effort to solve this problem by creating open, community-sourced expert-labeled datasets that capture information pertaining to class or pattern labels, bounding boxes and segmentation masks. In this paper we present the motivation, design and status of the ClimateNet dataset and associated model architecture. Authors: Karthik Kashinath (Lawrence Berkeley National Laboratory); Mayur Mudigonda (UC Berkeley); Kevin Yang (UC Berkeley); Jiayi Chen (UC Berkeley); Annette Greiner (Lawrence Berkeley National Laboratory); Mr Prabhat (Lawrence Berkeley National Laboratory)	Extreme Weather
ICML 2019	Improving Subseasonal Forecasting in the Western U.S. with Machine Learning (Research Track) Abstract and authors: (click to expand) Abstract: Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these long-term forecasts, the Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launched the Subseasonal Climate Forecast Rodeo, a year-long real-time forecasting challenge, in which participants aimed to skillfully predict temperature and precipitation in the western U.S. two to four weeks and four to six weeks in advance. We present and evaluate our machine learning approach to the Rodeo and release our SubseasonalRodeo dataset, collected to train and evaluate our forecasting system. Our predictive system is an ensemble of two regression models, and exceeds that of the top Rodeo competitor as well as the government baselines for each target variable and forecast horizon. Authors: Paulo Orenstein (Stanford); Jessica Hwang (Stanford); Judah Cohen (AER); Karl Pfeiffer (AER); Lester Mackey (Microsoft Research New England)	Extreme Weather Climate Science & Modeling
ICML 2019	Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructure (Research Track) Abstract and authors: (click to expand) Abstract: Zero Emission Vehicles (ZEV) play an important role in the decarbonization of the transportation sector. For a wider adoption of ZEVs, providing a reliable infrastructure is critical. We present a machine learning approach that uses unsupervised temporal clustering algorithm along with survey analysis to determine infrastructure performance and reliability of alternative fuels. We illustrate this approach for the hydrogen fueling stations in California, but this can be generalized for other regions and fuels. Authors: Kalai Ramea (PARC)	Transportation Societal Adaptation & Resilience
ICML 2019	A Flexible Pipeline for Prediction of Tropical Cyclone Paths (Research Track) Abstract and authors: (click to expand) Abstract: Hurricanes and, more generally, tropical cyclones (TCs) are rare, complex natural phenomena of both scientific and public interest. The importance of understanding TCs in a changing climate has increased as recent TCs have had devastating impacts on human lives and communities. Moreover, good prediction and understanding about the complex nature of TCs can mitigate some of these human and property losses. Though TCs have been studied from many different angles, more work is needed from a statistical approach of providing prediction regions. The current state-of-the-art in TC prediction bands comes from the National Hurricane Center at NOAA, whose proprietary model provides "cones of uncertainty" for TCs through an analysis of historical forecast errors. The contribution of this paper is twofold. We introduce a new pipeline that encourages transparent and adaptable prediction band development by streamlining cyclone track simulation and prediction band generation. We also provide updates to existing models and novel statistical methodologies in both areas of the pipeline respectively. Authors: Niccolo Dalmasso (Carnegie Mellon University); Robin Dunn (Carnegie Mellon University); Benjamin LeRoy (Carnegie Mellon University); Chad Schafer (Carnegie Mellon University)	Extreme Weather Climate Science & Modeling
ICML 2019	Mapping land use and land cover changes faster and at scale with deep learning on the cloud (Research Track) Abstract and authors: (click to expand) Abstract: Policymakers rely on Land Use and Land Cover (LULC) maps for evaluation and planning. They use these maps to plan climate-smart agriculture policy, improve housing resilience (to earthquakes or other natural disasters), and understand how to grow commerce in small communities. A number of institutions have created global land use maps from historic satellite imagery. However, these maps can be outdated and are often inaccurate, particularly in their representation of developing countries. We worked with the European Space Agency (ESA) to develop a LULC deep learning workflow on the cloud that can ingest Sentinel-2 optical imagery for a large scale LULC change detection. It’s an end-to-end workflow that sits on top of two comprehensive tools, SentinelHub, and eo-learn, which seamlessly link earth observation data with machine learning libraries. It can take in the labeled LULC and associated AOI in shapefiles, set up a task to fetch cloud-free, time series imagery stacks within the defined time interval by the users. It will pair the satellite imagery tile with it’s labeled LULC mask for the supervised deep learning model training on the cloud. Once a well-performing model is trained, it can be exported as a Tensorflow/Pytorch serving docker image to work with our cloud-based model inference pipeline. The inference pipeline can automatically scale with the number of images to be processed. Changes in land use are heavily influenced by human activities (e.g. agriculture, deforestation, human settlement expansion) and have been a great source of greenhouse gas emissions. Sustainable forest and land management practices vary from region to region, which means having flexible, scalable tools will be critical. With these tools, we can empower analysts, engineers, and decision-makers to see where contributions to climate-smart agricultural, forestry and urban resilience programs can be made. Authors: Zhuangfang Yi (Development Seed); Drew Bollinger (Development Seed); Devis Peressutti (Sinergise)	Ecosystems & Biodiversity Buildings Land Use
ICML 2019	Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling (Research Track) Abstract and authors: (click to expand) Abstract: Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-network models do not intrinsically conserve energy and mass, which is an obstacle to using them for long-term climate predictions. Here, we propose two methods to enforce linear conservation laws in neural-network emulators of physical models: Constraining (1) the loss function or (2) the architecture of the network itself. Applied to the emulation of explicitly-resolved cloud processes in a prototype multi-scale climate model, we show that architecture constraints can enforce conservation laws to satisfactory numerical precision, while all constraints help the neural-network better generalize to conditions outside of its training set, such as global warming. Authors: Tom G Beucler (Columbia University & UCI); Stephan Rasp (Ludwig-Maximilian University of Munich); Michael Pritchard (UCI); Pierre Gentine (Columbia University)	Climate Science & Modeling Extreme Weather
ICML 2019	The Impact of Feature Causality on Normal Behaviour Models for SCADA-based Wind Turbine Fault Detection (Research Track) Abstract and authors: (click to expand) Abstract: The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of work in how different input features affect the results. In this work, a new taxonomy based on the causal relations between the input features and the target is presented. Based on this taxonomy, the impact of different input feature configurations on the modelling and fault detection performance is evaluated. To this end, a framework that formulates the detection of faults as a classification problem is also presented. Authors: Telmo Felgueira (IST)	Power & Energy Heavy Industry and Manufacturing
ICML 2019	Predicting CO2 Plume Migration using Deep Neural Networks (Research Track) Abstract and authors: (click to expand) Abstract: Carbon capture and sequestration (CCS) is an essential climate change mitigation technology for achieving the 2 degree C target. Numerical simulation of CO2 plume migration in the subsurface is a prerequisite to effective CCS projects. However, stochastic high spatial resolution simulations are currently limited by computational resources. We propose a deep neural network approach to predict the CO2 plume migration in high dimensional systems with complex geology. Upon training, the network is able to give accurate predictions that are 6 orders of magnitude faster than traditional numerical simulators. This approach can be easily adopted to history-matching and uncertainty analysis problems to support the scale-up of CCS deployment. Authors: Gege Wen (Stanford University)	Carbon Capture & Sequestration
ICML 2019	Truck Traffic Monitoring with Satellite Images (Research Track) Abstract and authors: (click to expand) Abstract: The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low- and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries. Authors: Lynn Kaack (ETH Zurich); George H Chen (Carnegie Mellon University); Granger Morgan (Carnegie Mellon University)	Transportation
ICML 2019	Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions (Research Track) Abstract and authors: (click to expand) Abstract: Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc.. The recently available satellite-based observations give us a unique opportunity to directly build data-driven models to predict soil moisture instead of using land surface models, but previously there was no uncertainty estimate. We tested Monte Carlo dropout with an aleatoric term (MCD+A) for our long short-term memory models for this problem, and ask if the uncertainty terms behave as they were argued to. We show that MCD+A indeed gave a good estimate of our predictive error, provided we tune a hyperparameter and use a representative training dataset. The aleatoric term responded strongly to observational noise and the epistemic term clearly acted as a detector for physiographic dissimilarity from the training data. However, when the training and test data are characteristically different, the aleatoric term could be misled, undermining its reliability. We will also discuss some of the major challenges for which we anticipate the geoscientific communities will need help from computer scientists in applying AI to climate or hydrologic modeling. Authors: Chaopeng Shen (Pennsylvania State University)	Climate Science & Modeling Disaster Management and Relief Extreme Weather
ICML 2019	Detecting anthropogenic cloud perturbations with deep learning (Research Track) Best Paper Award Abstract and authors: (click to expand) Abstract: One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects. Authors: Duncan Watson-Parris (University of Oxford); Sam Sutherland (University of Oxford); Matthew Christensen (University of Oxford); Anthony Caterini (University of Oxford); Dino Sejdinovic (University of Oxford); Philip Stier (University of Oxford)	Computer Vision & Remote Sensing Climate Science & Modeling
ICML 2019	Data-driven surrogate models for climate modeling: application of echo state networks, RNN-LSTM and ANN to the multi-scale Lorenz system as a test case (Research Track) Abstract and authors: (click to expand) Abstract: Understanding the effects of climate change relies on physics driven computationally expensive climate models which are still imperfect owing to ineffective subgrid scale parametrization. An effective way to treat these ineffective parametrization of largely uncertain subgrid scale processes are data-driven surrogate models with machine learning techniques. These surrogate models train on observational data capturing either the embed- dings of their (subgrid scale processes’) underlying dynamics on the large scale processes or to simulate the subgrid processes accurately to be fed into the large scale processes. In this paper an extended version of the Lorenz 96 system is studied, which consists of three equations for a set of slow, intermediate, and fast variables, providing a fitting prototype for multi-scale, spatio-temporal chaos, and in particular, the complex dynamics of the climate system. In this work, we have built a data-driven model based on echo state net- works (ESN) aimed, specifically at climate modeling. This model can predict the spatio-temporal chaotic evolution of the Lorenz system for several Lyapunov timescales. We show that the ESN model outperforms, in terms of the prediction horizon, a deep learning technique based on recurrent neural network (RNN) with long short-term memory (LSTM) and an artificial neural network by factors between 3 and 10. The results suggest that ESN has the potential for being a powerful method for surrogate modeling and data-driven prediction for problems of interest to the climate community. Authors: Ashesh K Chattopadhyay (Rice University); Pedram Hassanzadeh (Rice University); Devika Subramanian (Rice University); Krishna Palem (Rice University); Charles Jiang (Rice University); Adam Subel (Rice University)	Climate Science & Modeling Extreme Weather
ICML 2019	Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy (Research Track) Abstract and authors: (click to expand) Abstract: According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to the environmental condition cannot be easily established. Remote sensing methods are increasingly being used to quantify and draw connections between rapidly changing climatic conditions and environmental impact. A crucial part of this analysis is processing spectroscopy data using radiative transfer models (RTMs) which is a computationally expensive process and limits their use with high volume imaging spectrometers. This work presents an algorithm that can efficiently emulate RTMs using neural networks leading to a multifold speedup in processing time, and yielding multiple downstream benefits. Authors: Shubhankar V Deshpande (Carnegie Mellon University), Brian D Bue (NASA JPL/Caltech), David R Thompson (NASA JPL/Caltech), Vijay Natraj (NASA JPL/Caltech), Mario Parente (UMass Amherst)	Computer Vision & Remote Sensing Climate Science & Modeling
ICML 2019	Planetary Scale Monitoring of Urban Growth in High Flood Risk Areas (Research Track) Abstract and authors: (click to expand) Abstract: Climate change is increasing the incidence of flooding. Many areas in the developing world are experiencing strong population growth but lack adequate urban planning. This represents a significant humanitarian risk. We explore the use of high-cadence satellite imagery provided by Planet, who’s flock of over one hundred ’Dove’ satellites image the entire earth’s landmass everyday at 3-5m resolution. We use a deep learning-based computer vision approach to measure flood-related humanitarian risk in 5 cities in Africa. Authors: Christian F Clough (Planet); Ramesh Nair (Planet); Gopal Erinjippurath (Planet); Matt George (Planet); Jesus Martinez Manso (Planet)	Extreme Weather Disaster Management and Relief
ICML 2019	Efficient Multi-temporal and In-season Crop Mapping with Landsat Analysis Ready Data via Long Short-term Memory Networks (Research Track) Abstract and authors: (click to expand) Abstract: Globe crop analysis from plentiful satellite images yields state-of-the-art results about estimating climate change impacts on agriculture with modern machine learning technology. Generating accurate and timely crop mapping across years remains a scientific challenge since existing non-temporal classifiers are hardly capable of capturing complicated temporal links from multi-temporal remote sensing data and adapting to interannual variability. We developed an LSTM-based model trained by previous years to distinguish corn and soybean for the current year. The results showed that LSTM outperformed random forest baseline in both in-season and end-of-the-season crop type classification. The improved performance is a result of the cumulative effect of remote sensing information that has been learned by LSTM model structure. The work pF(24rovides a valuable opportunity for estimating the impact of climate change on crop yield and early warning of extreme weather events in the future. Authors: Jinfan Xu (Zhejiang University); Renhai Zhong (Zhejiang University); Jialu Xu (Zhejiang University); Haifeng Li (Central South University); Jingfeng Huang (Zhejiang University); Tao Lin (Zhejiang University)	Agriculture & Food
ICML 2019	Autopilot of Cement Plants for Reduction of Fuel Consumption and Emissions (Deployed Track) Abstract and authors: (click to expand) Abstract: The cement manufacturing industry is an essential component of the global economy and infrastructure. However, cement plants inevitably produce hazardous air pollutants, including greenhouse gases, and heavy metal emissions as byproducts of the process. Byproducts from cement manufacturing alone accounts for approximately 5% of global carbon dioxide (CO2) emissions. We have developed "Autopilot" - a machine learning based Software as a Service (SaaS) to learn manufacturing process dynamics and optimize the operation of cement plants - in order to reduce the overall fuel consumption and emissions of cement production. Autopilot is able to increase the ratio of alternative fuels (including biowaste and tires) to Petroleum coke, while optimizing operation of pyro, the core process of cement production that includes the preheater, kiln and cooler. Emissions of gases such as NOx and SOx, and heavy metals such as mercury and lead which are generated through burning petroleum coke can be reduced through the use of Autopilot. Our system has been proven to work in real world deployments and an analysis of cement plant performance with Autopilot enabled shows energy consumption savings and a decrease of up to 28,000 metric tons of CO2 produced per year. Authors: Prabal Acharyya (Petuum Inc); Sean D Rosario (Petuum Inc); Roey Flor (Petuum Inc); Ritvik Joshi (Petuum Inc); Dian Li (Petuum Inc); Roberto Linares (Petuum Inc); Hongbao Zhang (Petuum Inc)	Heavy Industry and Manufacturing
ICML 2019	Towards a Sustainable Food Supply Chain Powered by Artificial Intelligence (Deployed Track) Honorable Mention Abstract and authors: (click to expand) Abstract: About 30-40% of food produced worldwide is wasted. This puts a severe strain on the environment and represents a $165B loss to the US economy. This paper explores how artificial intelligence can be used to automate decisions across the food supply chain in order to reduce waste and increase the quality and affordability of food. We focus our attention on supermarkets — combined with downstream consumer waste, these contribute to 40% of total US food losses — and we describe an intelligent decision support system for supermarket operators that optimizes purchasing decisions and minimizes losses. The core of our system is a model-based reinforcement learn- ing engine for perishable inventory management; in a real-world pilot with a US supermarket chain, our system reduced waste by up to 50%. We hope that this paper will bring the food waste problem to the attention of the broader machine learning research community. Authors: Volodymyr Kuleshov (Stanford University)	Heavy Industry and Manufacturing
ICML 2019	PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic Power Forecasting from Numerical Weather Prediction (Deployed Track) Abstract and authors: (click to expand) Abstract: Photovoltaic (PV) power generation has emerged as one of the leading renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather conditions like solar irradiance and temperature. Predicting PV production, even in the 24-hour forecast, remains a challenge and leads energy providers to left idling - often carbon-emitting - plants. In this paper, we introduce a Long-Term Recurrent Convolutional Network using Numerical Weather Predictions (NWP) to predict, in turn, PV production in the 24-hour and 48-hour forecast horizons. This network architecture fully leverages both temporal and spatial weather data, sampled over the whole geographical area of interest. We train our model on a prediction dataset from the National Oceanic and Atmospheric Administration (NOAA) to predict spatially aggregated PV production in Germany. We compare its performance to the persistence model and state-of-the-art methods. Authors: Johan Mathe (Frog Labs)	Power & Energy
ICML 2019	Finding Ship-tracks Using Satellite Data to Enable Studies of Climate and Trade Related Issues (Deployed Track) Abstract and authors: (click to expand) Abstract: Ship-tracks appear as long winding linear features in satellite images and are produced by aerosols from ship exhausts changing low cloud properties. They are one of the best examples of aerosol-cloud interaction experiments, which is currently the largest source of uncertainty in our understanding of climate forcing. Manually finding ship-tracks from satellite data on a large-scale is prohibitively costly while a large number of samples are required to better understand aerosol-cloud interactions. Here we train a deep neural network to automate finding ship-tracks. The neural network model generalizes well as it not only finds ship-tracks labeled by human experts, but also detects those that are occasionally missed by humans. It increases our sampling capability of ship-tracks by orders of magnitude and produces a first global map of ship-track distributions using satellite data. Major shipping routes that are mapped by the algorithm correspond well with available commercial data. There are also situations where commercial data are missing shipping routes that are detected by our algorithm. Our technique will enable studying aerosol effects on low clouds using ship-tracks on a large-scale, which will potentially narrow the uncertainty of the aerosol-cloud interactions. The product is also useful for applications such as coastal air pollution and trade. Authors: Tianle Yuan (NASA)	Transportation Climate Science & Modeling
ICML 2019	Using Smart Meter Data to Forecast Grid Scale Electricity Demand (Deployed Track) Abstract and authors: (click to expand) Abstract: Highly accurate electricity demand forecasts represent a major opportunity to create grid stability in light of the concurrent deployment of distributed renewables and energy storage, as well as the increasing occurrence of extreme weather events caused by climate change. We present an overview of a deployed machine learning system that accomplishes this task by using smart meter data (AMI) within the region governed by the Electric Reliability Council of Texas (ERCOT). Authors: Abraham Stanway (Amperon Holdings, Inc); Ydo Wexler (Amperon)	Power & Energy Extreme Weather
ICML 2019	Deep Learning for Wildlife Conservation and Restoration Efforts (Deployed Track) Abstract and authors: (click to expand) Abstract: Climate change and environmental degradation are causing species extinction worldwide. Automatic wildlife sensing is an urgent requirement to track biodiversity losses on Earth. Recent improvements in machine learning can accelerate the development of large-scale monitoring systems that would help track conservation outcomes and target efforts. In this paper, we present one such system we developed. 'Tidzam' is a Deep Learning framework for wildlife detection, identification, and geolocalization, designed for the Tidmarsh Wildlife Sanctuary, the site of the largest freshwater wetland restoration in Massachusetts. Authors: Clement Duhart (MIT Media Lab)	Ecosystems & Biodiversity
ICML 2019	Reinforcement Learning for Sustainable Agriculture (Ideas Track) Abstract and authors: (click to expand) Abstract: The growing population and the changing climate will push modern agriculture to its limits in an increasing number of regions on earth. Establishing next-generation sustainable food supply systems will mean producing more food on less arable land, while keeping the environmental impact to a minimum. Modern machine learning methods have achieved super-human performance on a variety of tasks, simply learning from the outcomes of their actions. We propose a path towards more sustainable agriculture, considering plant development an optimization problem with respect to certain parameters, such as yield and environmental impact, which can be optimized in an automated way. Specifically, we propose to use reinforcement learning to autonomously explore and learn ways of influencing the development of certain types of plants, controlling environmental parameters, such as irrigation or nutrient supply, and receiving sensory feedback, such as camera images, humidity, and moisture measurements. The trained system will thus be able to provide instructions for optimal treatment of a local population of plants, based on non-invasive measurements, such as imaging. Authors: Jonathan Binas (Mila, Montreal); Leonie Luginbuehl (Department of Plant Sciences, University of Cambridge); Yoshua Bengio (Mila)	Ecosystems & Biodiversity Extreme Weather Agriculture & Food
ICML 2019	Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem (Ideas Track) Honorable Mention Abstract and authors: (click to expand) Abstract: As global greenhouse gas emissions continue to rise, the use of geoengineering in order to artificially mitigate climate change effects is increasingly considered. Stratospheric aerosol injection (SAI), which reduces solar radiative forcing and thus can be used to offset excess radiative forcing due to the greenhouse effect, is both technically and economically feasible. However, naive deployment of SAI has been shown in simulation to produce highly adversarial regional climatic effects in regions such as India and West Africa. Wealthy countries would most likely be able to trigger SAI unilaterally, i.e. China, Russia or US could decide to fix their own climates and, by collateral damage, drying India out by disrupting the monsoon or inducing termination effects with rapid warming. Understanding both how SAI can be optimised and how to best react to rogue injections is therefore of crucial geostrategic interest. In this paper, we argue that optimal SAI control can be characterised as a high-dimensional Markov Decision Process. This motivates the use of deep reinforcement learning in order to automatically discover non-trivial, and potentially time-varying, optimal injection policies or identify catastrophic ones. To overcome the inherent sample inefficiency of deep reinforcement learning, we propose to emulate a Global Circulation Model using deep learning techniques. To our knowledge, this is the first proposed application of deep reinforcement learning to the climate sciences. Authors: Christian A Schroeder (University of Oxford); Thomas Hornigold (University of Oxford)	Solar Geoengineering Climate Science & Modeling
ICML 2019	Using Natural Language Processing to Analyze Financial Climate Disclosures (Ideas Track) Abstract and authors: (click to expand) Abstract: According to U.S. financial legislation, companies traded on the stock market are obliged to regularly disclose risks and uncertainties that are likely to affect their operations or financial position. Since 2010, these disclosures must also include climate-related risk projections. These disclosures therefore present a large quantity of textual information on which we can apply NLP techniques in order to pinpoint the companies that divulge their climate risks and those that do not, the types of vulnerabilities that are disclosed, and to follow the evolution of these risks over time. Authors: Sasha Luccioni (Mila); Hector Palacios (Element AI)	Climate Finance & Economics
ICML 2019	Machine Learning-based Maintenance for Renewable Energy: The Case of Power Plants in Morocco (Ideas Track) Abstract and authors: (click to expand) Abstract: In this project, the focus will be on the reduction of the overall electricity cost by the reduction of operating expenditures, including maintenance costs. We propose a predictive maintenance (PdM) framework for multi-component systems in renewables power plants based on machine learning (ML) and optimization approaches. This project would benefit from a real database acquired from the Moroccan Agency Of Sustainable Energy (MASEN) that own and operate several wind, solar and hydro power plants spread over Moroccan territory. Morocco has launched an ambitious energy strategy since 2009 that aims to ensure the energy security of the country, diversify the source of energy and preserve the environment. Ultimately, Morocco has set the target of 52% of renewables by 2030 with a large capital investment of USD 30 billion. To this end, Morocco will install 10 GW allocated as follows: 45% for solar, 42% for wind and 13% for hydro. Through the commitment of many actors, in particular in Research and Development, Morocco intends to become a regional leader and a model to follow in its climate change efforts. MASEN is investing in several strategies to reduce the cost of renewables, including the cost of operations and maintenance. Our project will provide a ML predictive maintenance framework to support these efforts. Authors: Kris Sankaran (Montreal Institute for Learning Algorithms); Zouheir Malki (Polytechnique Montréal); Loubna Benabou (UQAR); Hicham Bouzekri (MASEN)	Power & Energy
ICML 2019	GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery (Ideas Track) Abstract and authors: (click to expand) Abstract: Designing effective REDD+ policies, assessing their GHG impact, and linking them with the corresponding payments, is a resource intensive and complex task. GainForest leverages video prediction with remote sensing to monitor and forecast forest change at high resolution. Furthermore, by viewing payment allocation as a feature selection problem, GainForest can efficiently design payment schemes based on the Shapley value. Authors: David Dao (ETH); Ce Zhang (ETH); Nick Beglinger (Cleantech21); Catherine Cang (UC Berkeley); Reuven Gonzales (OasisLabs); Ming-Da Liu Zhang (ETHZ); Nick Pawlowski (Imperial College London); Clement Fung (University of British Columbia)	Climate Finance & Economics Forests
ICML 2019	Machine Intelligence for Floods and the Built Environment Under Climate Change (Ideas Track) Abstract and authors: (click to expand) Abstract: While intensification of precipitation extremes has been attributed to anthropogenic climate change using statistical analysis and physics-based numerical models, understanding floods in a climate context remains a grand challenge. Meanwhile, an increasing volume of Earth science data from climate simulations, remote sensing, and Geographic Information System (GIS) tools offers opportunity for data-driven insight and action plans. Defining Machine Intelligence (MI) broadly to include machine learning and network science, here we develop a vision and use preliminary results to showcase how scientific understanding of floods can be improved in a climate context and translated to impacts with a focus on Critical Lifeline Infrastructure Networks (CLIN). Authors: Kate Duffy (Northeastern University); Auroop Ganguly (Northeastern University)	Disaster Management and Relief Extreme Weather
ICML 2019	Predicting Marine Heatwaves using Global Climate Models with Cluster Based Long Short-Term Memory (Ideas Track) Abstract and authors: (click to expand) Abstract: Marine heatwaves make human and natural systems vulnerable to disaster risk through the disruption of ecological services and biological function. These extreme warming events in sea surface temperature are expected to become more frequent and longer lasting as a result of climate change. Large ensembles of global climate models now provide petabytes of climate-relevant data and an opportunity to probe machine learning to glean new insights about the climate conditions that cause marine heatwaves. Here we propose a k-means cluster based learning objective to map the geography of marine heatwave drivers globally to build a forecast for extreme sea surface temperatures using Long Short-Term Memory. We describe our machine learning approach to predict when and where future marine heatwaves will occur while leveraging the massive output of data from global climate models where traditional forecasting approaches fall short. The impacts of this work could warn coastal communities by providing a forecast for marine heatwaves, which would mitigate the negative effects on fishery productivity, ecosystem health, and tourism. Authors: Hillary S Scannell (University of Washington); Chris Fraley (Tableau Software); Nathan Mannheimer (Tableau Software); Sarah Battersby (Tableau Software); LuAnne Thompson (University of Washington)	Extreme Weather Climate Science & Modeling
ICML 2019	ML-driven search for zero-emissions ammonia production materials (Ideas Track) Abstract and authors: (click to expand) Abstract: Ammonia (NH3) production is an industrial process that consumes between 1-2% of global energy annually and is responsible for 2-3% of greenhouse gas emissions (Van der Ham et al.,2014). Ammonia is primarily used for agricultural fertilizers, but it also conforms to the US-DOE targets for hydrogen storage materials (Lanet al., 2012). Modern industrial facilities use the century-old Haber-Bosch process, whose energy usage and carbon emissions are strongly dominated by the use of methane as the combined energy source and hydrogen feedstock, not by the energy used to maintain elevated temperatures and pressures (Pfromm, 2017). Generating the hydrogen feedstock with renewable electricity through water electrolysis is an option that would allow retrofitting the billions of dollars of invested capital in Haber-Bosch production capacity. Economic viability is however strongly dependent on the relative regional prices of methane and renewable energy; renewables have been trending lower in cost but forecasting methane prices is difficult (Stehly et al., 2018; IRENA, 2017; Wainberg et al., 2017). Electrochemical ammonia production, which can use aqueous or steam H2O as its hydrogen source (first demonstrated ̃20years ago) is a promising means of emissions-free ammonia production. Its viability is also linked to the relative price of renewable energy versus methane, but in principle it can be significantly more cost-effective than Haber-Bosch (Giddeyet al., 2013) and also downscale to developing areas lacking ammonia transport infrastructure(Shipman & Symes, 2017). However to date it has only been demonstrated at laboratory scales with yields and Faradaic efficiencies insufficient to be economically competitive. Promising machine-learning approaches to fix this are discussed. Authors: Kevin McCloskey (Google)	Heavy Industry and Manufacturing Transportation
ICML 2019	Low-carbon urban planning with machine learning (Ideas Track) Abstract and authors: (click to expand) Abstract: Widespread climate action is urgently needed, but current solutions do not account enough for local differences. Here, we take the example of cities to point to the potential of machine learning (ML) for generating at scale high-resolution information on energy use and greenhouse gas (GHG) emissions, and make this information actionable for concrete solutions. We map the existing relevant ML literature and articulate ML methods that can make sense of spatial data for climate solutions in cities. Machine learning has the potential to find solutions that are tailored for each settlement, and transfer solutions across the world. Authors: Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC)); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))	Buildings Transportation
ICML 2019	The Grid Resilience & Intelligence Platform (GRIP) (Ideas Track) Abstract and authors: (click to expand) Abstract: Extreme weather events pose an enormous and increasing threat to the nation’s electric power systems and the associated socio-economic systems that depend on reliable delivery of electric power. The US Department of Energy reported in 2015, almost a quarter of unplanned grid outages were caused by extreme weather events and variability in the environment. Because climate change increases the frequency and severity of extreme weather events, communities everywhere will need to take steps to better prepare for, and if possible prevent major outages. While utilities have software tools available to help plan their daily and future operations, these tools do not include capabilities to help them plan for and recover from extreme events. Software for resilient design and recovery is not available commercially and research efforts in this area are preliminary. In this project, we are developing and deploying a suite of novel software tools to anticipate, absorb and recover from extreme events. The innovations in the project include the application of artificial intelligence and machine learning for distribution grid resilience, specifically, by using predictive analytics, image recognition and classification, and increased learning and problem-solving capabilities for the anticipation of grid events. Authors: Ashley Pilipiszyn (Stanford University)	Power & Energy Extreme Weather
ICML 2019	Meta-Optimization of Optimal Power Flow (Ideas Track) Abstract and authors: (click to expand) Abstract: The planning and operation of electricity grids is carried out by solving various forms of con- strained optimization problems. With the increasing variability of system conditions due to the integration of renewable and other distributed energy resources, such optimization problems are growing in complexity and need to be repeated daily, often limited to a 5 minute solve-time. To address this, we propose a meta-optimizer that is used to initialize interior-point solvers. This can significantly reduce the number of iterations to converge to optimality. Authors: Mahdi Jamei (Invenia Labs); Letif Mones (Invenia Labs); Alex Robson (Invenia Labs); Lyndon White (Invenia Labs); James Requeima (Invenia Labs); Cozmin Ududec (Invenia Labs)	Power & Energy Heavy Industry and Manufacturing
ICML 2019	Learning representations to predict landslide occurrences and detect illegal mining across multiple domains (Ideas Track) Abstract and authors: (click to expand) Abstract: Modelling landslide occurrences is challenging due to lack of valuable prior information on the trigger. Satellites can provide crucial insights for identifying landslide activity and characterizing patterns spatially and temporally. We propose to analyze remote sensing data from affected regions using deep learning methods, find correlation in the changes over time, and predict future landslide occurrences and their potential causes. The learned networks can then be applied to generate task-specific imagery, including but not limited to, illegal mining detection and disaster relief modelling. Authors: Aneesh Rangnekar (Rochester Institute of Technology); Matthew J Hoffman (Rochester Institute of Technology)	Disaster Management and Relief Land Use
ICML 2019	Harness the Power of Artificial intelligence and -Omics to Identify Soil Microbial Functions in Climate Change Projection (Ideas Track) Abstract and authors: (click to expand) Abstract: Contemporary Earth system models (ESMs) omit one of the significant drivers of the terrestrial carbon cycle, soil microbial communities. Soil microbial community not only directly emit greenhouse gasses into the atmosphere through the respiration process, but also release diverse enzymes to catalyze the decomposition of soil organic matter and determine nutrient availability for aboveground vegetation. Therefore, soil microbial community control over terrestrial carbon dynamics and their feedbacks to climate. Currently, inadequate representation of soil microbial communities in ESMs has introduced significant uncertainty in current terrestrial carbon-climate feedbacks. Mitigation of this uncertainty requires to identify functions, diversity, and environmental adaptation of soil microbial communities under global climate change. The revolution of -omics technology allows high throughput quantification of diverse soil enzymes, enabling large-scale studies of microbial functions in climate change. Such studies may lead to revolutionary solutions to predicting microbial-mediated climate-carbon feedbacks at the global scale based on gene-level environmental adaptation strategies of the microbial community. A key initial step in this direction is to identify the biogeography and environmental adaptation of soil enzyme functions based on the massive amount of data generated by -omics technologies. Here we propose to make this step. Artificial intelligence is a powerful, ideal tool for this leap forward. Our project is to integrate Artificial intelligence technologies and global -omics data to represent climate controls on microbial enzyme functions and mapping biogeography of soil enzyme functional groups at global scale. This outcome of this study will allow us to improve the representation of microbial function in earth system modeling and mitigate uncertainty in current climate projection. Authors: Yang Song (Oak Ridge National Lab); Dali Wang (Oak Ridge National Lab); Melanie Mayes (Oak Ridge National Lab)	Climate Science & Modeling Carbon Capture & Sequestration Ecosystems & Biodiversity