Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model

Problem — This work addresses the gap in existing literature regarding the integration of predictive modeling and optimization for aerial wildfire suppression. Current methods often lack a comprehensive framework that accounts for both fire spread prediction and the strategic design of aerial interventions. The authors highlight that existing models do not effectively incorporate operational and environmental uncertainties, which are critical for real-world applications. This paper is a preprint and has not undergone peer review.

Method — The authors propose a hybrid modeling framework that combines a convolutional neural network (CNN) with cellular automata to simulate wildfire spread. The CNN processes terrain, fuel, and wind data to predict spatially varying fire behavior. The intervention module employs a gradient-based optimization approach to determine binary drop actions, with continuous-valued parameters for location and orientation mapped to a simulation grid. The model distinguishes between water and retardant, each with unique suppression effects: immediate reduction of active burning and a persistent decrease in future spread. The framework incorporates both aleatoric uncertainty, quantified through Monte Carlo sampling of fire-state realizations, and epistemic uncertainty, assessed via spatially correlated prediction-error perturbations.

Results — The proposed framework was evaluated using a case study based on the 2020 Bear Fire. The results demonstrate that the model can generate coherent aerial suppression schedules that significantly reduce the total fire-affected area. Specifically, the framework achieved a reduction of up to 30% in the fire-affected area compared to baseline strategies that do not account for uncertainty. The authors provide a detailed analysis of the robustness of the suppression plans, indicating that the uncertainty-aware approach leads to more effective intervention strategies.

Limitations — The authors acknowledge several limitations, including the reliance on the accuracy of the input data (terrain, fuel, and wind) and the assumptions made in the hybrid model. They also note that the model’s performance may vary under different environmental conditions not represented in the training data. Additionally, the computational cost of Monte Carlo sampling may limit the scalability of the approach in real-time applications. The authors do not address potential challenges related to the integration of this framework into existing wildfire management systems.

Why it matters — This research has significant implications for the field of wildfire management, particularly in enhancing the effectiveness of aerial suppression strategies. By providing a robust framework that integrates predictive modeling with optimization under uncertainty, this work paves the way for more informed decision-making in wildfire interventions. The findings can inform future research on adaptive management strategies in the face of climate change and increasing wildfire frequency, as published in arXiv cs.LG.