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<p>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 interpretability and robustness, explainability, uncertainty, and sensitivity analyses were conducted on the best-performing stacking model using SHapley Additive exPlanations (SHAP), Monte Carlo uncertainty analysis, and Sobol global sensitivity analysis. SHAP results identified temperature, soil moisture, distance to villages, and relative humidity as the most influential wildfire conditioning factors. Monte Carlo simulations (1000 iterations) yielded a mean AUC of 0.847, indicating stable model performance under input perturbations. Sobol sensitivity analysis further confirmed soil moisture and temperature as the most influential variables, with total-order sensitivity indices of 0.45 and 0.21, respectively. Spatial analysis revealed that approximately 20.75% of the study area falls within high to very high wildfire susceptibility zones, primarily associated with regions characterized by steep terrain, low soil moisture conditions, and significant anthropogenic influence. Overall, the study presents an interpretable and uncertainty-aware GeoAI framework that integrates ensemble learning with explainable artificial intelligence and sensitivity analysis, providing a reproducible approach for wildfire susceptibility assessment in complex mountainous environments.</p>

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An explainable GeoAI framework for spatial assessment of wildfire susceptibility in the Upper Ravi sub-basin, Indian Himalaya

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月7日

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  • Suheb,
  • Md Nawazuzzoha,
  • Md Shahid Ali,
  • Md. Mamoon Rashid,
  • Darakhsha Fatma Naqvi,
  • Honey Qaisar,
  • Pierre Sicard,
  • Shankar Karuppannan,
  • Hasan Raja Naqvi

摘要

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 interpretability and robustness, explainability, uncertainty, and sensitivity analyses were conducted on the best-performing stacking model using SHapley Additive exPlanations (SHAP), Monte Carlo uncertainty analysis, and Sobol global sensitivity analysis. SHAP results identified temperature, soil moisture, distance to villages, and relative humidity as the most influential wildfire conditioning factors. Monte Carlo simulations (1000 iterations) yielded a mean AUC of 0.847, indicating stable model performance under input perturbations. Sobol sensitivity analysis further confirmed soil moisture and temperature as the most influential variables, with total-order sensitivity indices of 0.45 and 0.21, respectively. Spatial analysis revealed that approximately 20.75% of the study area falls within high to very high wildfire susceptibility zones, primarily associated with regions characterized by steep terrain, low soil moisture conditions, and significant anthropogenic influence. Overall, the study presents an interpretable and uncertainty-aware GeoAI framework that integrates ensemble learning with explainable artificial intelligence and sensitivity analysis, providing a reproducible approach for wildfire susceptibility assessment in complex mountainous environments.