The state of Sikkim in India has many steep slopes and has been susceptible to landslides. Since 1968 there have been innumerable losses of lives and properties due to landslides. There is an urgent need for advance assessment of degrees of vulnerability and delineation of the most vulnerable zone for shifting of the population and infrastructure to a safer zone. The identification and formulation of most suitable and acceptable method for such assessment is still nascent and research based. In this study, an attempt has been made to integrate the concept of Shannon's entropy with the information value-based statistical model to evaluate the landslide susceptibility in the study area and assess the improvement made through the integration of Shannon's entropy by comparing the results with the landslide susceptibility determined from the information value-based statistical model alone. Initially, the thematic layers pertaining to all the causative parameters were overlaid with the help of geographical information system that resulted in the formation of 78,256 numbers of polygons for each one of which landslide susceptibility was determined. For each polygon, the total landslide information value (TLIV) was computed as the summation of the landslide information values determined for the individual sub-categories present within the respective polygons. Again for each polygon, the Shannon's entropy value of the individual parameters was multiplied with the summation of the landslide information values of all the sub-categories present within the respective parameters. The product values computed for the different causative parameters were summed up to determine the total landslide information value with entropy (TLIV_e). Finally, the entire study area was categorized into five zones of landslides susceptibility based on the TLIV and TLIV_e, respectively. The prediction accuracy of the landslides determined based on the landslide susceptibility derived from TLIV_e was found to be significantly high (91 %) as compared to that derived from TLIV (85 %) indicating the potential contribution of Shannon's entropy in the improved delineation of the landslide susceptibility zones.

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The Likelihood Ratio (LR) Model has been applied as an improvement upon the Frequency Ratio (FR) that computes the ratio of the percentage of the landslide pixels to the percentage of the non-landslide pixels instead of the total number of pixels used in the denominator as in case of the FR. The comparative assessment of the two techniques is made through spatial modelling of GIS vector data using the ArcGIS software. Two different Landslide Information Values were computed for each polygon element of the study area employing the two FR techniques that categorized the study area into five classes of vulnerability using natural breaks (Jenks) technique. Subsequently, vulnerability zonation maps were prepared showing the different levels of landslide vulnerability. The LR technique yielded significantly higher vulnerability assessment accuracy (77%) as compared to the standard FR (71%).