结合神经网络与元胞自动机，优化空中灭火投放策略以减少火区面积。
Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
- 用混合模型预测地形、燃料和风力下的火势扩散
- 通过梯度优化生成精准的空中投掷位置与方向，分水与阻燃剂不同效果
- 支持不确定性分析，适合需决策鲁棒性的消防规划者
空中灭火不仅需要预测火势蔓延，还需在操作与环境不确定条件下设计有效干预策略。本文提出一个融合混合神经-元胞自动机火势模型与基于梯度的目标化空中洒水设计的建模与优化框架。火势模型利用地形、可燃物与风力数据预测空间异质的蔓延行为；干预模块则确定二值化洒水动作，连续参数映射至仿真网格，区分水（即时抑制燃烧）与阻燃剂（持续降低未来蔓延）的作用。为评估策略鲁棒性，采用蒙特卡洛采样量化随机不确定性，并通过空间相关预测误差扰动分析认知不确定性。基于2020年熊火事件的案例研究显示，该框架能生成连贯的空中灭火计划，有效减少总受灾面积，并支持不确定性感知的干预策略分析。
原文摘要 · Abstract (English)
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.
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