A Probabilistic Landslide Risk Assessment (LRA) on NH31A and Settlement in Rorachu watershed, East Sikkim, India by using bivariate models and geospatial techniques
Authors: Alam Sk Asraful , Mandal Sujit , Maity Ramkrishna 
Published: April 2022

Abstract
The Sikkim Himalaya has been identified as a zone with a high risk of slope instability. The NH 31A route runs through the east Sikkim Himalaya, which has been severely affected by landslides. Over the last few years, landslide occurrences have engulfed the NH 31A road portions and town, as well as the adjacent countryside. Landslide susceptibility zones (LSZ) and landslide risk assessment (LRA) are an urgent and safe mitigation tool for local residents to help with strategic planning in the event of a landslide. The current study aims to use a bivariate statistical method termed frequency ratio (FR), information value (IV), and certainty factor (CF) analysis for LSZ and LRA maps in Rorachu watershed, East Sikkim, in order to determine the triggering reasons for the LSI and LRA. This is the first time this work has been done in the NH31A and settlement areas in east Sikkim. Early reports, aerial photographs, Google Earth images, and repeated field visits were used to create the landslide inventory map. A total of 153 landslide locations were mapped using GIS software, with 70% (107) being utilised as training data for modelling using the FR, IV, and CF models, and the remaining 30% (46) being used to validate the models. The thirteen landslide causative factors Geology, Soil, Elevations, Slope, Curvature, Drainage Density (DD), Road Density (RD), Rainfall, Normalize Difference Vegetation Index (NDVI), Land Use Land Cover (LULC), Topographic Position Index (TPI), Stream Power Index (SPI), and Topographic Wetness Index (TWI) were extracted from a spatial database for LSZ mapping using the FR, IV, and CF The histogram and density plot have been used to test the landslide susceptibility zonation (LSM) map, that elicited the preponderance of the triggering conditions for landslides in the Rorachu watershed. The slope (35o to 50o), heights (2,500–4,100 m), and rainfall (2000–2,500 mm and 3,000–3,300 mm) were found to have the highest proportion and density for landslides. Receive operating characteristics (ROC) curve, Success rate curve (SRC), and landslide density (LD) method analysis are all used to validate the predicted frequency ratio (FR), information value (IV), and certainty factor (CF) model. For frequency ratio (FR), information value (IV), and certainty factor (CF) models, the AUC for success rate curves (SRC) are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.80%), respectively. The AUC for prediction rates for the FR, IV, and CF models are 0.828 (82.80%), 0.750 (%), and 0.836 (83.60%), respectively. The landslide risk assessment (LRA) is revealed that the most significant risk of settlements places are FR (20.72%), IV (40.91%), and CF (20.90%), and the most significant danger of NH 31A road is FR (20.72%), IV (40.91%), and CF (20.90%) (18.78%). These landslide susceptibility maps and landslide risk assessment (LRA) maps can be used to generate land use planning strategies, saving people's lives and also being beneficial for planners and mitigation. Highway construction, deforestation, and urbanization should be given special consideration.