RESEARCH ARTICLES CURRENT SCIENCE, VOL. 108, NO. 9, 10 MAY 2015 1662 *For correspondence. (e-mail: rohananadi@yahoo.com) Landslide susceptibility zonation of Tehri reservoir rim region using binary logistic regression model Rohan Kumar* and R. Anbalagan Department of Earth Sciences, Indian Institute of Technology, Roorkee 247 667, India A remote sensing and GIS based landslide susceptibi- lity zonation (LSZ) of the Tehri reservoir rim region has been presented here. Landslide causal factors such as land use/land cover, photo-lineaments, landslide incidences, drainage, slope, aspect, relative relief, topog- raphic wetness index and stream power index were derived from remote sensing data. Ancillary data in- cluded published geological map, soil map and topo- graphic map. Correlation between factor classes and landslides was computed using binary logistic regres- sion model and a probability estimate of landslide occurrence on cell-by-cell basis for the entire study area was obtained. The probability map was further classified into very low, low, moderate, high and very high susceptible zones using statistical class break technique. Accuracy assessment of the model was per- formed using ROC curve technique, which in turn gave acceptable 80.2% accuracy. LSZ indicates that the area immediate to the reservoir side slope is highly prone to landslides. Keywords: Logistic regression, landslide susceptibility zonation, remote sensing, reservoir rim. SCIENTIFIC research regarding the process involved, prior planning and mitigation strategies for natural hazard pheno- menon is given much emphasis nowadays. This is attrib- uted to the fact that there is a substantial increase in the frequency of natural hazards and consequent fatalities. Such fatalities are directly related to the human interfer- ence in natural processes. Some glaring examples of the same are the 2012 Japan tsunami and 2013 Kedarnath floods in Uttarakhand, India. Among the different types of natural hazards, landslides are the most dominant and consistent hazardous phenomena in mountainous regions. Particularly in the Himalayan terrain which is geody- namically active, problems have been substantiated with increasing anthropogenic activities. Tehri dam (260.5 m high) is built at the confluence of the Bhagirathi and Vilangana rivers in the Lesser Hima- laya. A 67 km long, huge reservoir is present on the up- stream side of the dam. Several studies have indicated that the reservoir has induced negative impact on the geo- environmental system of the rim area 1 . A number of villages are situated all around the rim of the reservoir. Due to readjustment of slopes during drawdown conditions of the reservoir, the slopes on which villages are located have been rendered unstable in many areas in addition to loss of huge areas of farmland. Geo-environmental factors such as slope, relative relief, hydrogeological condition, lithology and structural discontinuity are responsible for slope instability in the hilly region 2,3 . Characterization of landslide causative factors and comprehensive landslide probability mapping are the most important planning strategies for mitigation. A landslide susceptibility zonation (LSZ) map is pre- pared in advance to facilitate mitigation strategies in the wake of any landslide hazard in future. It provides prior knowledge of probable landslide zones on the basis of a set of geo-environmental factors suitable for landslide locally. LSZ is based on the analogy that future land- slides are expected at those locations which have the same set of geo-environmental conditions as those of past and present landslide locations 2,4,5 . Choice of factors depends upon the exhaustive field work, data availability and professional experience. Advent of machine learning, fast computation packages, easy data availability and GIS have propelled the landslide hazard research to a new high. The outcome can be seen in terms of the quantum of literature regarding landslide hazard owing to different methodologies available at present. Broadly, landslide susceptibility methods can be classified into qualitative, semi-quantitative and quantitative. Qualitative methods are based on weights and scores of casual factors synthe- sized from professional knowledge and are subjective in nature. For regional analysis of landslide susceptibility, qualitative method is suitable 6 . Semi-quantitative meth- ods assume weight and score of factors/classes computed from logical tools, such as analytical hierarchy process (AHP), weighted linear combination (WLC), etc. These methods are partially subjective and feasible in LSZ at both small scale and large scale 7–9 . Quantitative methods are based on statistical correlation between factors and landslide inventory and are of two types – bivariate and multivariate. Bivariate statistical methods are based on correlation between factors/classes and the landslide