Hybrid Machine Learning and SBAS-InSAR Integration for Landslide Susceptibility Mapping Along the Balakot–Naran Route, Pakistan

Natural hazards such as landslides are among the most harmful and recurring hazards to infrastructure, communities, and the environment around the world. In Pakistan, the Balakot Valley is prone to severe landslides, especially along the Balakot–Naran route, which is a major economic and tourist route. This route requires accurate landslide susceptibility mapping (LSM) to mitigate landslide risk. However, existing approaches mainly rely on statistical methods, which do not sufficiently address the complexity of spatial patterns and characteristics between landslide conditioning factors (LCFs) and their prevalence. In this study, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements of slope deformation (Vslope) were employed to update the landslide inventory. Following this update, an LSM was generated to examine the causal variables that are associated with landslide occurrences. Several machine learning (ML) classifiers, which include Adaptive Boosting (AdaBoost), Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and a hybrid (ADA + LGBM + XGB), are utilized for mapping landslide susceptibility. A total of 14 LCFs were considered, with 70% of the dataset being trained and 30% tested. To evaluate the significance of these variables, Recursive Feature Elimination (RFE) and the Shapley Additive Explanations (SHAP) were used. Results indicate that the hybrid model exhibits superior efficiency in the area under the curve (AUC) (88.00%), precision (84.69%), accuracy (84.52%), F1-score (84.69%), and recall (84.70%). The hybrid classifier, when combined with InSAR predictions, generates an improved LSM for the route. In conclusion, the improved LSM can effectively identify areas that are prone to landslides along the Balakot–Naran route.