Deep reinforcement learning for optimal firebreak placement in forest fire prevention

Abstract

The increasing frequency and intensity of large wildfires have become a significant natural hazard, requiring the development of advanced decision-support tools for resilient landscape design. Existing methods, such as Mixed Integer Programming and Stochastic Optimization, while effective, are computationally demanding. In this study, we introduce a novel Deep Reinforcement Learning (DRL) methodology to optimize the strategic placement of firebreaks across diverse landscapes. We employ Deep Q-Learning, Double Deep Q-Learning, and Dueling Double Deep Q-Learning, integrated with the Cell2Fire fire spread simulator and Convolutional Neural Networks. Our DRL agent successfully learns optimal firebreak locations, demonstrating superior performance compared to heuristics, especially after incorporating a pre-training loop. This improvement ranges between 1.59%–1.7% with respect to the heuristic, depending on the size of the instance, and 4.79%–6.81% when compared to a random solution. Our results highlight the potential of DRL for fire prevention, showing convergence with favorable results in cases as large as 40 × 40 cells. This study represents a pioneering application of reinforcement learning to fire prevention and landscape management.