🤖 AI Summary
This study addresses the challenge of wildfire risk assessment in Iran’s arid and semi-arid regions. We developed a multi-source geospatial machine learning model integrating climatic, topographic, land-use, and anthropogenic variables. Methodologically, we employed XGBoost coupled with SHAP-based interpretability analysis, Sentinel-2/Landsat remote sensing classification, and GIS-based spatial interpolation. We propose—novel for Iran—the first nationwide, interpretable wildfire susceptibility classification framework explicitly incorporating both natural and human drivers, alongside a drought-adapted, multi-scale feature engineering paradigm. The model achieves an AUC of 0.92; it identifies 12 high-risk hotspot zones and improves spatial prediction accuracy by 27% over conventional logistic regression. These contributions provide a scientifically robust foundation and methodological reference for national wildfire prevention planning in Iran.