做火灾应急规划或AI优化决策的团队值得关注——这套框架把预测和干预统一优化，比传统分步方法更高效，且能处理环境不确定性。
该研究提出了一种结合混合神经网络-元胞自动机火灾模型与梯度优化方法的空中灭火规划框架。模型利用地形、燃料和风数据预测火灾蔓延，并通过连续参数优化确定空中投放位置和方向。水和阻燃剂分别模拟即时灭火和持久抑制效果。基于2020年Bear Fire的案例验证表明，该框架能生成有效的空中灭火计划，减少火灾影响面积，并支持不确定性分析。
Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
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.