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Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression

Shaurya Mathur, Shreyas Bellary Manjunath, Nitin Kulkarni, Alina Vereshchaka

TL;DR

FireCastRL addresses the shift from reactive to proactive wildfire response by integrating spatiotemporal ignition forecasting with reinforcement learning–driven suppression in a physics-informed 3D environment. The framework combines a large-scale $9.5$ million sample dataset with a CNN–LSTM ignition predictor and a PPO-based helitack controller operating in a 3D terrain model, enabling end-to-end threat assessment and decision support. Quantitative results on ignition forecasting (accuracy $73.1\%$; Palisades confidence $98.6\%$) and RL-driven suppression (closely matched or superior to baselines) demonstrate the approach’s potential for improved containment and resource planning. By releasing the dataset and providing a web app, the work offers practical tools for researchers and decision-makers to develop and test proactive wildfire response strategies.

Abstract

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.

Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression

TL;DR

FireCastRL addresses the shift from reactive to proactive wildfire response by integrating spatiotemporal ignition forecasting with reinforcement learning–driven suppression in a physics-informed 3D environment. The framework combines a large-scale million sample dataset with a CNN–LSTM ignition predictor and a PPO-based helitack controller operating in a 3D terrain model, enabling end-to-end threat assessment and decision support. Quantitative results on ignition forecasting (accuracy ; Palisades confidence ) and RL-driven suppression (closely matched or superior to baselines) demonstrate the approach’s potential for improved containment and resource planning. By releasing the dataset and providing a web app, the work offers practical tools for researchers and decision-makers to develop and test proactive wildfire response strategies.

Abstract

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 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.
Paper Structure (30 sections, 3 equations, 3 figures, 4 tables)

This paper contains 30 sections, 3 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Wildfire Prediction
  4. Fire Spread Simulation
  5. RL for Disaster Mitigation
  6. Proposed Framework
  7. Data Acquisition and Preparation
  8. Wildfire Incident Collection and Filtering
  9. Dataset Balancing
  10. Spatiotemporal Feature Extraction
  11. Wildfire Ignition Forecasting
  12. Suppression Strategies Optimization using RL
  13. Physics-informed Simulation
  14. Fire Engine and Spread Modeling
  15. Fuel Models and Parameters
  16. ...and 15 more sections

Figures (3)

  • Figure 1: FireCastRL framework is a wildfire forecasting and mitigation system containing five stages: (A) acquisition of IRWIN database and GRIDMET weather data, (B) prediction of wildfire ignition using CNN-LSTM architecture, (C) simulation of RL-based helitack suppression based on real-world terrain and environmental data, (D) generation of fire threat assessment report with risk forecasts and strategic recommendations, and (E) web-based application.
  • Figure 2: The architecture of the CNN & Bi-LSTM based neural network model for wildfire prediction.
  • Figure 3: Architecture of the custom PPO policy network. It combines multi-scale CNNs, a spatial attention module, and LSTM layers for capturing temporal fire dynamics.