Explainable XGBoost–SHAP Modeling Coupled with GeoDetector for Forest-Fire Occurrence Susceptibility and Future Climate Sensitivity in the Dabie Mountains

Forest-fire occurrence susceptibility prediction remains challenging in mountainous regions because fire occurrence is controlled by nonlinear interactions among climatic, vegetation, topographic, and anthropogenic factors. Developing interpretable prediction models that integrate multi-source environmental information is essential for improving susceptibility assessment and understanding predictor contributions. Using 2003–2022 MODIS active-fire records and multi-source environmental data from the Dabie Mountains, this study developed an explainable XGBoost-based forest-fire occurrence susceptibility prediction framework. The predictive performance of XGBoost was compared with Random Forest and radial basis function support vector machine (RBF-SVM) using five-fold cross-validation, and TreeSHAP was applied to interpret predictor contributions, nonlinear responses, and interactions. KDE, standard deviational ellipses (SDEs), and GeoDetector were further used to characterize historical fire patterns and complementary spatial associations, while an independent climate-scale XGBoost model was applied to evaluate future susceptibility sensitivity under SSP126 and SSP585 scenarios. XGBoost achieved an ROC-AUC of 0.850, and log-transformed population showed the largest global SHAP contribution. GeoDetector identified precipitation and relative humidity as the strongest spatial associations with historical fire-density patterns across KDE bandwidths. The climate-scale model achieved a pooled five-fold ROC-AUC of 0.830 and indicated stronger late-century susceptibility-score redistribution under SSP585 than under SSP126. Together, these results demonstrate an explainable susceptibility modeling approach that integrates predictive modeling, model interpretation, spatial analysis, and future climate assessment. The outputs represent relative susceptibility and scenario-specific sensitivity responses rather than unconditional fire probabilities or deterministic forecasts.