GIS complemented statistical classification techniques yield good result in predicting landslide hazards. Indian standard landslide hazard model follows guidelines formulated by the Bureau of Indian Standards (BIS, 1998), in which the study area is divided into five categories, ranging from very low hazard zone to very high hazard zone on fixed numerical ratings. For land use planners, “moderate hazard zone” proves vague and indecisive. In the present study, BIS based landslide hazard zones are demarcated for 140 sq. km area for a road corridor in East and North Sikkim that shows 21.96%, 53.14%, 22.80% and 2.10% for ‘Low Hazard Zone’, ‘Moderate Hazard Zone’, ‘High Hazard Zone’ and ‘Very High Hazard Zone’ respectively. This classification scheme has been reclassified to binary system based on population distribution and defining the cut-off by evaluation techniques of the ROC. The reclassification eliminates “moderate hazard zone”, minimizing the Type-II error and becomes more acceptable for future land use planning.

Understanding the causes of slope development with movement initiation of land sliding requires knowledge on dynamicity, displacement, strain concentration and factor of safety. The 13th mile landslide on Gangtok-Nathula road of the Sikkim Himalaya has seriously affected the Indo-China trade route. To quantify the spatial movement pattern, strain analysis and identification of zones of safety were attempted which indicates that differential movement activity of the landslide zone is co-relatable with differential strain pattern with an overall imprint of the Himalaya collision tectonics.

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Modeling landslide susceptibility is one of the important aspects of land use planning and risk management. Several modeling methods are available based either on highly specialized knowledge on causative attributes or on good landslide inventory data to use as training and testing attribute on model development. Understandably, these two criteria are rarely available for local land regulators. This paper presents a new model methodology, which requires minimum knowledge of causative attributes and does not depend on landslide inventory. As landslide causes due to the combined effect of causative attributes, this model utilizes communality (common variance) of the attributes, extracted by exploratory factor analysis and used for calculation of landslide susceptibility index. The model can understand the inter-relationship of different geo-environmental attributes responsible for landslide along with identification and prioritization of attributes on model performance to delineate non-performing attributes. Finally, the model performance is compared with the well established AHP method (knowledge driven) and FRM method (data driven) by cut-off independent ROC curves along with cost-effectiveness. The model shows it’s performance almost at par with the established models, involving minimum modeling expertise. The findings and results of the present work will be helpful for the town planners and engineers on a regional scale for generalized planning and assessment.