Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: A study on Kurseong region of Darjeeling Himalaya, India

Abstract

The main objectives of the current study is to generate and compare the landslide susceptibility maps (LSMs) using a probabilistic model i.e. weight of evidence (WoE), a machine learning technique i.e. support vector machine (SVM) and ensemble approach i.e. WoE-SVM. Experiments were conducted in the Kurseong region, a part of Darjeeling Himalaya as a study area. Previous landslide sites were identified through field survey and previous records. Total 273 landslide sites were compiled and considered for the landslide inventory map to calibrate as well as validate the models. Seventeen landslide conditioning factors (LCFs) were used for landslide susceptibility modelling, i.e. slope degree, elevation, aspect, curvature, rainfall, geological structure, lineament, distance to main roads, soil texture, soil depth, earthquake zone, land-use/land-cover, drainage density, stream power index, sediment transport index, and topographic wetness index. The generated landslide susceptibility maps were validated applying the receiver operating characteristic curves (ROC), Positive Predictive Value (PPV), Proportion Correctly Classified (PCC) and the seed cell area index (SCAI). The results revealed that prediction rates are 89.47%, 91.81%, and 94.45% with standard error 0.0359, 0.0289 and 0.0183 respectively for WoE, SVM and ensemble WoE-SVM models. The study shows that the fellow land, plantation area, roads side areas and denudation structural hills having elevation more than 1500 m and 24° to 69° slopes are extremely susceptible to landslides. The comparison of the produced landslide maps revealed that all the applicable models have committed accuracy for studying sensitivity in Kurseong region of Darjeeling Himalaya. The ensemble model has better capability than the other two models. The results may be helpful for landslide risk mitigation in the Kurseong and its surrounding region having similar terrain region and geological conditions.