Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya,India

Landslides cause heavy damage to property and infrastructure, in addition to being responsible for the loss of human lives, in many parts of the Himalaya. It is possible to take appropriate management measures to reduce the risk from potential landslide hazard with the help of landslide hazard zonation (LHZ) maps. The present work is an attempt to utilize binary logistic regression analysis for the preparation of a landslide susceptibility map for a part of Garhwal Himalaya, India, which is highly prone to landslides, by taking the geological, geomorphological and topographical parameters into consideration. Remote sensing and the geographic information system (GIS) were found to be very useful in the input database preparation, data integration and analysis stages. The coefficients of the predictor variables are estimated using binary logistic regression analysis and are used to calculate the landslide susceptibility for the entire study area within a GIS environment. The receiver operator characteristic curve analysis gives 88.7% accuracy for the developed model.