Landslide susceptibility assessment for the Darjeeling Toy Train route: a GIS and machine learning approach

Landslide susceptibility mapping is crucial for reducing risks in culturally and historically significant areas like the Darjeeling Toy Train route, a UNESCO World Heritage site. In this study, the risk of landslides along this road is evaluated using Geographic Information System (GIS) tools and advanced machine learning models, such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Logistic Regression, and Classification and Regression Trees (CART). It uses a set of 512 landslide and non-landslide sites, with a 70:30 split between training and testing. Within the research area, thirteen topographical, hydrological, and geological factors linked to landslides are shown as GIS layers to make maps of landslide susceptibility (LSM). The study area particularly vulnerable to various types of landslides, including debris slides, rock falls, and soil slips. ROC-AUC results show that the SVM model did the best (0.813), followed by GBM (0.807), Logistic Regression (0.797), and CART (0.781). SVM had the highest accuracy rate at 83.2%, followed by GBM at 81.5% and LR at 80.3%. CART had the lowest overall accuracy rate at 78.6%. Furthermore, confusion matrix analysis showed that SVM and Logistic Regression were better at finding actual landslide-prone areas, with 84.6% and 82.1% recall rates, respectively. This made them more accurate in predicting high-risk areas. Susceptibility levels were categorized, revealing high-risk areas like Darjeeling and Rishihat and safer areas like Kurseong and Mohanbari. For lowering the risk of landslides and protecting this historic route, these results are very useful for land management and disaster preparation.