用AI预测火灾并自动调度直升机灭火，提升响应效率
Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression
- 结合时空深度模型预测火源，用强化学习指挥直升机实时扑救
- 在3D物理仿真中实现高风险火灾的智能战术部署
- 公开950万条环境数据集，适合应急与气候研究者使用
野火频发且强度加剧，每年给美国造成数十亿美元的扑救成本和经济损失。传统管理以被动应对为主。本文提出FireCastRL框架，融合深度时空模型进行野火起火预测；对高风险预测，调用预训练强化学习代理，在物理驱动的三维模拟环境中指挥直升机灭火单元执行实时扑救策略。系统生成威胁评估报告，辅助应急人员优化资源配置与规划。同时，我们公开一个大规模时空数据集，包含950万条环境变量样本，用于野火预测研究。该工作展示了深度学习与强化学习协同支持火灾预报与战术响应的潜力。
原文摘要 · Abstract (English)
Wildfires are growing in frequency and intensity, devastating ecosystems and communities while causing billions of dollars in suppression costs and economic damage annually in the U.S. Traditional wildfire management is mostly reactive, addressing fires only after they are detected. We introduce \textit{FireCastRL}, a proactive artificial intelligence (AI) framework that combines wildfire forecasting with intelligent suppression strategies. Our framework first uses a deep spatiotemporal model to predict wildfire ignition. For high-risk predictions, we deploy a pre-trained reinforcement learning (RL) agent to execute real-time suppression tactics with helitack units inside a physics-informed 3D simulation. The framework generates a threat assessment report to help emergency responders optimize resource allocation and planning. In addition, we are publicly releasing a large-scale, spatiotemporal dataset containing $\mathbf{9.5}$ million samples of environmental variables for wildfire prediction. Our work demonstrates how deep learning and RL can be combined to support both forecasting and tactical wildfire response. More details can be found at https://sites.google.com/view/firecastrl.
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