Landslide Susceptibility Mapping Through Hyperparameter Optimized Bagging and Boosting Ensembles: Case Study of NH-10, West Bengal, India

Landslides along highways are unanticipated obstacles in hilly regions as the happening of which results in connectivity detachment of hilly areas from plains. Due to landslides, the lives of human beings get endangered along with economic loss to a large extent. Susceptibility assessment and mapping are crucial aspects of the mitigation and management of landslide catastrophes in a region. This study was undertaken in a quest to assess the performance of the bagging ensemble (Random Forest) and boosting ensemble (extreme gradient boosting) in susceptibility mapping for landslide along the NH-10 (previously NH-31A), from Sivok (West Bengal) to Rangpoo (Sikkim), which is approximately 51.5 km. In total, eleven geo-environmental factors were taken into account for performing the study under consideration. A total of 309 landslide locations were identified for the assessment, 70% of which were considered to be training samples and the remaining 30% to be testing samples. Feature importance was computed with the game theory-based SHapley Additive exPlanations (SHAP) approach. Evaluation of fulfillment of the proposed methods was accomplished with area under the receiver operating characteristics curve (AUROC) in which the Random Forest and extreme gradient boosting methodologies attained 99.17% and 99.45% accuracy, respectively, with the training samples, whereas the attained accuracies for validation datasets were 95.86% (Random Forest) and 96.58% (extreme gradient boosting), respectively. The constructed maps will be beneficial to government policy- and decision-makers in the region to deal with and mitigate the landslide catastrophes.