A comparative study using bivariate statistical method and knowledge-driven heuristic approach for the comparison of landslide susceptibility mapping in West Sikkim district of Sikkim Himalaya, India

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.