G. S. Rawat’s research while affiliated with Hemwati Nandan Bahuguna Garhwal University and other places

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.

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.

Weights of evidence method, which is basically the Bayesian approach in a log-linear form, using the prior probability of occurrence of an event like landslide, helps to find out its posterior probability based on the relative contributions of evidential themes which are influential in creating slope instability. In the present study, this method has been used to find out the probability of occurrence of landslides for unique combinations of evidential themes and to prepare a landslide hazard zonation map of part of Bhagirathi valley, Uttarakhand, within a Geographic Information System environment. Lithology, structure, slope, slope aspect, land use/land cover, drainage and distance to road are the evidential themes considered in the study. The model has been further validated using receiver operator characteristic curve analysis, which shows an accuracy of 84.6%.