Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
English summary
Ion Matei et al. present a framework for aerial wildfire suppression planning that integrates a hybrid neural-cellular automaton fire spread model with gradient-based optimization. The model predicts spatially varying fire behavior from terrain, fuel, and wind inputs, while the intervention module decides binary drop actions with continuous location and orientation parameters. Water and retardant are represented distinctly, reducing active burning immediately or persistently lowering future spread. Aleatoric uncertainty is captured via Monte Carlo sampling of daily fire states, and epistemic uncertainty via spatially correlated prediction-error perturbations. A case study on the 2020 Bear Fire demonstrates the framework's ability to generate coherent suppression schedules and support uncertainty-aware strategy analysis.
Chinese summary
Ion Matei等人提出了一个空中野火抑制规划框架，整合了混合神经-元胞自动机火灾蔓延模型与基于梯度的优化方法。该模型根据地形、燃料和风输入预测空间变化的火灾行为，干预模块决定具有连续位置和方向参数的二元投放动作。水和阻燃剂被分别表示为立即减少活跃燃烧和持续降低未来蔓延的不同抑制效果。偶然不确定性通过每日火情状态的蒙特卡洛采样量化，认知不确定性通过空间相关的预测误差扰动量化。基于2020年熊火的案例研究表明，该框架能生成连贯的空中抑制时间表，并支持对干预策略进行不确定性分析。
Key points
A hybrid CNN-cellular automaton model predicts fire spread from terrain, fuel, and wind data.
混合CNN-元胞自动机模型根据地形、燃料和风数据预测火灾蔓延。
The optimization module decides binary aerial drop actions with continuous location and orientation, distinguishing water and retardant effects.
优化模块决定具有连续位置和方向的二元空中投放动作，并区分水和阻燃剂的不同效果。
The framework quantifies both aleatoric uncertainty (Monte Carlo sampling) and epistemic uncertainty (spatially correlated perturbations).
框架量化了偶然不确定性（蒙特卡洛采样）和认知不确定性（空间相关扰动）。
A 2020 Bear Fire case study shows effective suppression schedules and uncertainty-aware strategy analysis.
2020年熊火案例研究展示了有效的抑制时间表和不确定性感知的策略分析。