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: November 2021

The Sikkim Himalaya has been recognized as region enormously susceptible slope instability. The NH 31A road falls with east Sikkim Himalaya which has highly deformed by numerous landslide events. Over the few years the NH 31A road sections and settlement with its surrounding areas are invaded by landslide events. To resolve the problem connected to landslide, landslide susceptibility zones (LSZ) and landslide risk assessment (LRA) is an urgent and safe mitigation measure to helping the strategic planning for local people. The present study is an endeavor to take advantage of bivariate statistical method called frequency ratio (FR), information value (IV) and certainty factor (CF) analysis for LSZ and LRA map and attempt to get out the triggering factors for the LSI and LRA in Rorachu watershed, East Sikkim. The landslide inventory map was made by the more premature reports, aerial photograph, Google Earth image and multiple field visits. A total 153 landslides location were mapped using GIS software and divided into 70 % (107) for training data for the modeling using FR, IV and CF models and remaining 30 % (46) were used for validating 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 spatial database for the LSZ mapping using FR, IV and CF models. The landslide susceptibility zonation (LSM) map also tested by the histogram and density plot, this is elicited most of the triggering factors for the landslides in Rorachu watershed. The results have been showing that the slope (35o to 50o), elevations (2,500 – 4,100 m) and rainfall (2000- 2,500 mm and 3,000 – 3,300 mm) is the intensest concentration and density for the landslides. The predictive frequency ratio (FR), information value (IV) and certainty factor (CF) model has been validated by receive operating characteristics (ROC) curve, Success rate curve (SRC) and landslide density (LD) method analysis. The result shows that AUC for success rate curves (SRC) are 0.925 (92.50 %), 0.846 (84.60 %) and 0.868 (86.80 %), respectively for frequency ratio (FR), information value (IV) and certainty factor (CF) models. And the result shows that AUC for prediction rates are 0.828 (82.80 %), 0.750 (75 %) and 0.836 (83.60 %), respectively for the FR, IV and CF models. The element-at-risk (Settlement and Road) is revealed the landslide risk assessment (LRA) have been showing that the most significant risk of settlements areas by the model of FR (9%), IV (38.59%) and CF (20.90%) and the most significant risk of NH 31A road is FR (20.72%), IV (40.91%) and CF (18.78%). These landslide susceptibility maps and landslide risk assessment (LRA) map can be used for the development of land use planning strategies, saves human loss and important for the planners and mitigation purpose. So remarkable attention should be taken into consideration for the highway construction, deforestation and urbanization.