Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach

The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F -score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F -score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides.