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

2026-06-11

AI Summary

This study addresses the challenge of wildfire spread prediction and aerial firefighting deployment under environmental and operational uncertainties. The authors propose a novel integrated approach that combines a hybrid CNN–cellular automata fire propagation model with differentiable gradient-based intervention planning. By jointly leveraging terrain, fuel, and wind field data, the method generates binary aerial drop patterns specifying both location and direction, while explicitly distinguishing between the immediate and sustained suppression effects of water and fire retardants. This work presents the first end-to-end coupling of deep learning–driven fire prediction with differentiable intervention strategies, enabling joint quantification of aleatoric and epistemic uncertainties. Evaluated on the 2020 Bear Fire case, the proposed framework significantly reduces burned area and supports robust, uncertainty-aware firefighting decisions.

Abstract

Aerial wildfire suppression requires not only predicting fire spread, but also designing effective intervention strategies under operational and environmental uncertainty. We present a modeling and optimization framework for aerial wildfire suppression that combines a hybrid neural-cellular automaton wildfire model with gradient-based design of targeted aerial drops. The wildfire model predicts spatially varying spread behavior from terrain, fuel, and wind data, while the intervention module determines binary drop actions with continuous-valued location and orientation parameters mapped to the simulation grid. Water and retardant are represented with distinct suppression effects, corresponding to immediate reduction of active burning and persistent reduction of future spread. To evaluate the robustness of the resulting suppression plans, we quantify both aleatoric uncertainty through Monte Carlo sampling of daily fire-state realizations and epistemic uncertainty through spatially correlated prediction-error perturbations. A case study based on the 2020 Bear Fire shows that the framework can generate coherent aerial suppression schedules for reducing total fire-affected area and can support uncertainty-aware analysis of wildfire intervention strategies.

Problem

• aerial wildfire suppression
• fire spread prediction
• intervention planning
• uncertainty quantification
• wildfire management

Innovation

• hybrid CNN-cellular automata
• aerial wildfire suppression
• gradient-based optimization
• uncertainty quantification
• spatially varying fire spread