Firecastrl Achieves Proactive Wildfire Prediction and Suppression, Reducing Billion-Dollar Damages

Wildfires increasingly threaten ecosystems and economies, incurring substantial suppression costs and damage each year. Shaurya Mathur, Shreyas Bellary Manjunath, and Nitin Kulkarni, all from the University at Buffalo, alongside Alina Vereshchaka, present a novel proactive approach to wildfire management with their FireCastRL framework. This research significantly advances the field by integrating deep spatiotemporal wildfire forecasting with reinforcement learning to intelligently deploy helitack suppression tactics within a realistic 3D simulation. By predicting ignition risks and optimising resource allocation, FireCastRL promises to shift wildfire management from reactive response to proactive prevention, and the authors are also making a large-scale dataset publicly available to further accelerate research in this critical area.

For predictions indicating high risk, the framework deploys a pre-trained reinforcement learning agent within a physics-informed 3D simulation, allowing it to execute real-time suppression tactics using virtual helitack units. This sophisticated simulation environment incorporates realistic terrain, elevation data, and mesoscale wind fields, providing a highly accurate representation of wildfire behaviour. This work establishes a novel approach to wildfire management, combining the predictive power of deep learning with the tactical decision-making capabilities of reinforcement learning.

The deep spatiotemporal forecasting model accurately predicts wildfire ignition based on historical environmental data, enabling proactive intervention before fires escalate. The physics-informed 3D simulation, coupled with a browser-based interface, allows the reinforcement learning agent to learn optimal helitack deployment strategies in dynamic and realistic wildfire scenarios. Utilising the Proximal Policy Optimization (PPO) algorithm, the agent refines its suppression tactics through repeated simulations, maximising effectiveness and minimising resource expenditure. This end-to-end system represents a significant advancement over existing methods, which often focus solely on detection or fire spread prediction without integrating downstream strategic response planning. The publicly available dataset, derived from GRIDMET and IRWIN, will undoubtedly accelerate research in wildfire forecasting and AI-driven disaster mitigation, potentially saving billions of dollars in suppression costs and economic damage annually.

FireCastRL development using GRIDMET and IRWIN

This work directly responds to the $14.7 billion in direct property losses and over $3 billion in suppression costs incurred in 2023 alone, with total annual economic burdens potentially reaching $893 billion. This publicly released dataset facilitates advancements in wildfire forecasting and AI-driven disaster response, providing a robust foundation for predictive modelling. This model accurately forecasts potential ignition points, enabling proactive intervention before fires escalate. Subsequently, the team constructed a physics-informed wildfire simulation environment, meticulously incorporating real-world land cover, elevation data, and mesoscale wind fields.

This engine combines a browser-based fire simulator, allowing for real-time interaction with a reinforcement learning (RL) agent tasked with controlling helitack units. Scientists deployed a pre-trained RL agent, trained using the Proximal Policy Optimization (PPO) algorithm, to execute real-time suppression tactics within this simulated 3D environment. The agent learns to strategically deploy aerial suppression resources, optimizing containment efforts in dynamic wildfire scenarios. Finally, the framework generates a comprehensive fire threat assessment report for emergency responders. This report details predicted ignition coordinates, projected burn trajectories, the optimal suppression sequence, and actionable response recommendations. This end-to-end pipeline represents a significant methodological innovation, seamlessly integrating predictive modelling with tactical response planning, and ultimately enabling more effective and efficient wildfire management.

FireCastRL predicts wildfire ignition with deep learning

Experiments revealed the framework successfully predicts wildfire ignition using a deep learning model trained on this extensive dataset. The model, a hybrid CNN-LSTM architecture, forecasts ignition based on a 75-day meteorological and environmental context window, treating the prediction as a binary classification problem. Each sample within the dataset is represented by a vector of 12 meteorological features, including precipitation (pr), relative humidity max/min (rmax/rmin), and wind speed (vs), enabling the model to learn both short-term triggers and long-term stress signals. Data shows the dataset comprises 126,800 samples, including 50,720 positive (ignition) and 76,080 negative (non-ignition) instances.

Researchers meticulously constructed the dataset by combining high-resolution incident data from the Integrated Reporting of Wildfire Information (IRWIN) database, encompassing 348,604 wildfire reports from January 2014 to April 2025, with meteorological sequences from GRIDMET. A multi-stage filtering procedure isolated 50,720 unique ignition events, retaining only incidents ≥5km apart and enforcing a minimum 2-hour gap between retained ignitions. To address the inherent class imbalance, a three-tier negative sampling framework generated 76,080 realistic non-wildfire samples, including far, near, and yearly negatives. The breakthrough delivers a physics-informed 3D simulation environment where a pre-trained reinforcement learning agent deploys helitack suppression strategies for high-risk predictions. This simulation utilizes real-world elevation, land cover, and mesoscale wind data to create a realistic sandbox for optimizing suppression tactics. This work demonstrates how deep learning and reinforcement learning can be combined to support both forecasting and tactical wildfire response.

FireCastRL learns proactive wildfire suppression strategies through reinforcement

The framework utilises a deep spatiotemporal model to predict potential ignition points, and then employs reinforcement learning to simulate and optimise the deployment of suppression tactics using helitack units within a detailed 3D environment. Researchers trained a deep learning model on over 9.5 million real-world data samples to accurately forecast likely wildfire ignition locations. Following this, a reinforcement learning agent was developed to learn effective fire suppression techniques within these.