Physics-guided spatiotemporal neural models for fuel density prediction
For wildfire management practitioners, this provides a computationally efficient and physically plausible forecasting method to support adaptive prescribed burn planning.
This work introduces a physics-guided machine learning framework for fuel density prediction that integrates physics constraints into deep learning models, achieving improved accuracy and stability over purely data-driven baselines.
This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowledge into deep learning models to enhance model accuracy and stability. We explore three deep learning architectures -- ConvLSTM, Adaptive Fourier Neural Operator (AFNONet), and Video Vision Transformer (ViViT) -- to model the spatiotemporal evolution of fuel density. Our approach incorporates differentiable physics-informed terms in the loss function, including a mass-conserving fuel transport term and a rate-of-spread estimation. Experimental results, averaged across multiple independent trials, demonstrate that the proposed PGML framework outperforms purely data-driven baselines without physics constraints in both accuracy and stability. This framework enables computationally efficient, physically plausible fire forecasting to support adaptive prescribed burn management.