Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

TL;DR

This paper presents a convolutional recurrent deep learning model capable of accurately predicting time-resolved wildfire spread over 24 hours, demonstrating stability and high accuracy across simulated and real-world datasets.

Contribution

It introduces a novel deep learning approach for time-resolved wildfire modeling that maintains stability and accuracy over extended prediction periods.

Findings

Achieves Jaccard scores between 0.89 and 0.94 on fire scar predictions.

Demonstrates stable wildfire propagation over 24 hours in simulations.

Effective across diverse datasets, including real-world topologies.

Abstract

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques offer the ability to bridge these objectives by learning first-principles physics while achieving computational speedup. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. In this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 minute increments. We demonstrate the model in application to three simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 hours of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar.