Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915741524033536 |
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| author | Mathur, Shaurya Manjunath, Shreyas Bellary Kulkarni, Nitin Vereshchaka, Alina |
| author_facet | Mathur, Shaurya Manjunath, Shreyas Bellary Kulkarni, Nitin Vereshchaka, Alina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14238 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression Mathur, Shaurya Manjunath, Shreyas Bellary Kulkarni, Nitin Vereshchaka, Alina Machine Learning 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. |
| title | Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.14238 |