Wildfire Susceptibility Mapping in Greece Using Ensemble Machine Learning

This study explores the use of ensemble machine learning models to develop wildfire susceptibility maps (WFSMs) in Greece, focusing on their application as regressors. We provide a continuous assessment of wildfire risk, enhancing the interpretability and accuracy of predictions. Two key metrics were developed: Ensemble Mean and Ensemble Max. This dual-metric approach improves predictive robustness and provides critical insights for wildfire management strategies. The ensemble mode effectively handles complex, high-dimensional data, addressing challenges such as over fitting and data heterogeneity. Utilizing advanced techniques like XGBoost, GBM, LightGBM, and CatBoost regressors, our research demonstrates the potential of these methods to enhance wildfire risk estimation. The Ensemble Mean model classified 50% of the land as low risk and 21% as high risk, while the Ensemble Max model identified 38% as low risk and 33% as high risk. Notably, 83% of wildfires between 2000 and 2024 occurred in areas marked as high-risk by both models. The findings reveal that a significant proportion of wildfires occurred in areas identified as high risk by both ensemble models, underscoring their effectiveness. This approach offers significant potential to mitigate wildfires’ environmental, economic, and social impacts, enhance climate resilience, and strengthen preparedness for future wildfire events.