Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region

• Published in: Earth science informatics 2024-12, Vol.17 (6), p.5709-5731
• Main Author: Tonbul, Hasan
• Format: Article
• Language: English
• Subjects: Anthropogenic factors; Artificial intelligence; Drought; Drought index; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Ecosystems; Explainable artificial intelligence; Forest & brush fires; Forest fires; Information Systems Applications (incl.Internet); Land surface temperature; Machine learning; Mapping; Ontology; Risk assessment; Simulation and Modeling; Soil analysis; Soil moisture; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Statistical tests; Surface temperature
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-024-01480-7

Forest fires pose a serious risk to ecosystems in the Mediterranean region; thus, 2021 fires in the Mediterranean region of Türkiye emphasize the requirement for accurate and interpretable forest fire susceptibility (FFS) mapping. This study presents an innovative approach to FFS mapping for the Mersin, Antalya, and Mugla provinces, integrating machine learning (ML) models with Explainable Artificial Intelligence (XAI). The methodology employs three state-of-the-art ML models: eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Light Gradient-Boosting Machine (LightGBM). These models generated FFS maps using 14 fire conditioning factors, including meteorological, topographic, environmental, and anthropogenic factors. LightGBM demonstrated outstanding performance, acquiring the highest accuracy (0.897), outperforming GBM (0.881) and XGBoost (0.851). McNemar’s statistical test demonstrated significant differences in the predictive capabilities between XGBoost and both GBM and LightGBM, whereas no significant difference was found between GBM and LightGBM. Information Gain and SHapley Additive exPlanations (SHAP) analyses were applied to enhance model interpretability and validate feature importance. Both methods agreed that the most influential variables in FFS are soil moisture, Palmer Drought Severity Index (PDSI), and Land Surface Temperature (LST). On the other hand, SHAP plots revealed complex, nonlinear relationships between these factors and fire susceptibility. At the same time, a high increase in LST enhances the risk of fires; higher soil moisture values and the PDSI decrease the possibility of fire risk. This research also contributes to the concept of FFS mapping interpretability and operational utility with the application of XAI, which establishes a transparent basis for identifying fire risk drivers in Mediterranean ecosystems.