Landslides are among the top five natural disasters in terms of casualties and property damage; therefore, landslide susceptibility mapping is vital in landslide-prone areas, particularly hilly terrain. Globally, landslides alone take away the life of 17% of the total death caused by natural hazards. The death count in numbers is approximately 1000 per year, with property damage of about US$ 4 billion. This makes the study of landslides extremely significant. Important factors contributing to the reported global increase in landslides are the rapid growth of the world's population, urbanisation in the developing world, and global climate change.Landslides are natural and anthropogenic hazards that have impacted Indian subcontinent, especially the Himalayas and other mountainous areas. Comparative evaluations of the landslide susceptibility mapping models are necessary for landslide susceptibility mapping to find the best fit model for the specific area. The present study has been conducted in the West Sikkim district of India, in the Indian Himalayan Region, using a data-driven statistical model of information value method (IVM) and frequency ratio method (FRM), as well as a knowledge-driven heuristic approach of analytic hierarchy process (AHP). The combination of the statistical and the knowledge-based approach is applied because the former gives the unbiased result based on the pixel value of the satellite data used, whereas the knowledge-based method gives the value based on the knowledge and experience of the expert, so a very good comparison can be made. In this study, eleven landslide conditioning factors were analysed in the remote sensing (RS) and geographic information system (GIS) environment, which are slope aspect, slope gradient, slope curvature, drainage density, elevation, lithology, land use and land cover (LULC), normalised difference vegetation index (NDVI), geomorphology, lineament density, and soil type. The Resourcesat 2A satellite images were used from Indian remote sensing agency having LISS 4 sensors of 5.8 m resolution data.A total of 685 landslides were identified in a satellite image, and the polygons of the same in the shapefile format mapped in the GIS environment. Landslides mapped from the satellite data has also been validated in the google earth images and selected sites are also validated by ground truthing. 70% of the total landslide polygons were taken as the training data the remaining 30% landslide polygons were taken for the validation purpose The studies were validated using a receiver operating characteristic curve that fit the model with acceptable values of more than 60% for all three models, with the highest value of 74% being obtained for the information value method. The density distribution method has validated the result, confirmed by the landslide density increased from the low susceptibility zone to the high susceptibility zone. These types of studies are helpful for the decision-makers and the planners for the developmental projects that are ongoing in the state and future projects.

Slope failures along the road sections cause huge problems every year in Sikkim. A slope instability analyses was performed along the road sections in parts of West Sikkim. The study was supported by geotechnical analyses like rock mass rating (RMR) and slope mass rating (SMR). In order to identify different failure types stereographic plotting based kinematic analyses was performed. Out of 33 study sites for the geotechnical analyses, 24 sites were identified as wedge failure and the remaining 9 sites were identified as the planar failure. Highly metamorphosed rocks like quartzite, phyllite and gneiss were the common rocks found in the area, comprising the discontinuities like joints were found up to 4 sets in certain locations. Other tectonic discontinuities include main central thrust (MCT), Tista lineament and other inferred thrust. Several such structural discontinuities and the weak lithology have made the area extremely vulnerable. The RMR_basic values varies from 26–60 in the study area which falls in the good to average category rocks. Adjustment factors were added to the RMR_basic to calculate the SMR values, which ranges between 26–74 and falls in good to bad rock mass description. Based on the different values for the SMR ratings, supportive measures were recommended for the different study locations. These are spot or systematic bolting, spot shotcrete, systematic anchors, systematic shotcrete, toe wall, dental concrete, systematic reinforced shotcrete, re-excavation, deep drainage, etc. and bioengineering treatment.