Adding physics-guided loss terms to ConvLSTM, AFNONet, and ViViT improves fuel density prediction accuracy and stability over purely data-driven baselines on simulated prescribed-fire data.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
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abstract
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.
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Physics-guided spatiotemporal neural models for fuel density prediction
Adding physics-guided loss terms to ConvLSTM, AFNONet, and ViViT improves fuel density prediction accuracy and stability over purely data-driven baselines on simulated prescribed-fire data.