Harnessing AI for Wildfire Defense: An approach to Predict and Mitigate Global Fire Risk

Harnessing AI for Wildfire Defense: An approach to Predict and Mitigate Global Fire Risk (Papers Track)

Hassan Ashfaq (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology)

NeurIPS 2024 Recorded Talk Cite

Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring Ecosystems & Biodiversity Extreme Weather Forests Active Learning Generative Modeling Interpretable ML Reinforcement Learning

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.