Abstract

Wildfires pose an escalating threat to ecosystems and human settlements, making accurate forecasting essential for early mitigation. This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor-Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. The models were trained and evaluated using historical data from Chile (2000-2023), including wildfire occurrences, meteorological variables, topography, and vegetation indices. After preprocessing and class balancing, each model was tested over 100 experimental runs. All models achieved outstanding performance, with F1-Scores exceeding 0.999 and perfect AUC-ROC scores. The Transformer model showed a slight advantage over the CNN (99.94%) and Actor-Critic DRL (99.93%) in accuracy. Feature importance analysis identified wind speed, temperature, and vegetation indices as the most influential variables. While DRL offers theoretical benefits for adaptive decision-making, Transformer architectures more effectively capture spatiotemporal dependencies in wildfire dynamics. The findings can support the integration of deep learning models into early warning systems, contributing to proactive wildfire risk management. Future work will include validation with diverse regional datasets, real-time deployment, and collaboration with emergency response agencies.