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.

翻译：野火发生的频率和强度日益增长，不仅破坏生态系统和社区，每年在美国还造成数十亿美元的灭火成本和经济损失。传统的野火管理大多是被动的，仅在火灾被探测到后才采取应对措施。本文提出 \textit{FireCastRL}，一种结合野火预测与智能灭火策略的主动式人工智能框架。该框架首先使用深度时空模型预测野火起火点。针对高风险预测，我们部署预训练的强化学习智能体，在基于物理原理的三维仿真环境中指挥直升机灭火单元执行实时灭火战术。该框架生成威胁评估报告，以帮助应急响应人员优化资源分配与规划。此外，我们公开了一个包含 $\mathbf{9.5}$ 百万个样本、用于野火预测的环境变量时空数据集。本研究展示了如何结合深度学习与强化学习来同时支持野火预测与战术响应。更多细节请访问 https://sites.google.com/view/firecastrl。