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Venue Title
NeurIPS 2024 Enhancing Reinforcement Learning-Based Control of Wave Energy Converters Using Predictive Wave Modeling (Papers Track)
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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)
NeurIPS 2024 Icy Waters: Developing a Test-Suite to Benchmark Sea Ice Concentration Forecasting (Papers Track)
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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)
NeurIPS 2024 Regional Ocean Forecasting with Hierarchical Graph Neural Networks (Papers Track)
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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)
NeurIPS 2024 Multi-scale decomposition of sea surface height snapshots using machine learning (Papers Track)
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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)
NeurIPS 2024 Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures (Papers Track)
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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)
NeurIPS 2024 A Hybrid Machine Learning Model For Ship Speed ThroughWater: Solve And Predict (Proposals Track)
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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)
ICLR 2024 Improving Streamflow Predictions with Vision Transformers (Papers Track)
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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)
ICLR 2024 Reconstructing the Breathless Ocean with Spatio-Temporal Graph Learning (Papers Track)
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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)
ICLR 2024 Exploring Graph Neural Networks to Predict the Seagrasses Ecosystem State in the Italian Seas (Papers Track)
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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)
ICLR 2024 Planning for Floods & Droughts: Intro to AI-Driven Hydrological Modeling (Tutorials Track)
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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)
NeurIPS 2023 Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning (Papers Track)
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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)
NeurIPS 2023 Global Coastline Evolution Forecasting from Satellite Imagery using Deep Learning (Papers Track)
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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)
NeurIPS 2023 Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models (Papers Track)
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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)
NeurIPS 2023 Decarbonizing Maritime Operations: A Data-Driven Revolution (Proposals Track)
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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)
ICLR 2023 SEA LEVEL PROJECTIONS WITH MACHINE LEARNING USING ALTIMETRY AND CLIMATE MODEL ENSEMBLES (Papers Track)
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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)
ICLR 2023 Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics (Papers Track)
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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)
ICLR 2023 Graph-Based Deep Learning for Sea Surface Temperature Forecasts (Papers Track)
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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)
ICLR 2023 Green AutoML for Plastic Litter Detection (Papers Track)
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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)
NeurIPS 2022 Optimizing toward efficiency for SAR image ship detection (Papers Track)
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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)
NeurIPS 2022 Towards Low Cost Automated Monitoring of Life Below Water to De-risk Ocean-Based Carbon Dioxide Removal and Clean Power (Proposals Track)
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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)
NeurIPS 2022 Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times (Proposals Track)
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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)
AAAI FSS 2022 Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms
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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)