Wildfires have emerged as a significant environmental concern in the Himalayan region, particularly in the Upper Ravi sub-basin of Himachal Pradesh, India. This study aims to map wildfire susceptibility by integrating Geographic Information Systems (GIS), remote sensing data, and advanced ensemble machine learning techniques. A total of sixteen biophysical and anthropogenic conditioning factors, including topography, climatic variables, vegetation indices, and human activity indicators, were used to develop wildfire susceptibility models. Five machine learning algorithms were evaluated, including Random Forest, XGBoost, LightGBM, CatBoost, and a stacking ensemble model. Among these, the stacking model demonstrated the best predictive performance with an AUC of 0.95.To enhance model interpr…

Conclusion The results demonstrate that ensemble machine learning approaches provide strong predictive capability for wildfire susceptibility modelling in complex mountainous environments. Among the evaluated models, the stacking ensemble model achieved the best performance, indicating its effectiveness in capturing nonlinear relationships among wildfire conditioning factors. The integration of SHapley Additive exPlanations (SHAP), Monte Carlo uncertainty analysis, and Sobol global sensitivity analysis enabled a comprehensive interpretation of model predictions and provided insights into the relative importance and uncertainty associated with the conditioning variables. The wildfire susceptibility map indicates that approximately 20.75% of the Upper Ravi sub-basin falls within high to very high susceptibility zones, primarily located in areas characterized by steep terrain, lower soil moisture conditions, and significant anthropogenic influence. The spatial distribution of wildfire susceptibility reflects the combined influence of climatic, topographic, and human-related factors that govern fire occurrence in mountainous landscapes. The SHAP analysis improved the interpretability o…

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