Landslide is one of the important disasters taking place on earth, which may be either a natural or man-made process. Landslides are more active and disastrous in hilly and mountainous regions. The present study aims to identify the landslide susceptibility areas in the Relli river basin in Darjeeling Himalaya using logistic regression (LR) and frequency ratio (FR) models. The GIS techniques have been used for landslide susceptibility mapping. A total number of 67 landslide locations have been identified from Google Earth images and multiple field surveys. 70% of landslide locations have been randomly selected and used as training data set for preparing landslide susceptibility map, and the remaining 30% have been used as validation data set. For the present study, 20 different factors like drainage density, drainage texture, infiltration number, stream frequency, stream junction frequency, stream power index, lithology, soil, relative relief, slope, maximum relief, drainage intensity, ruggedness number, rainfall, dissection index, aspect, relief class, and distance from stream, topographic wetness index and land use land cover have been used. The application of the logistic regression and frequency ratio model has demonstrated that the lower catchment of the basin has been widely dominated by the most landslide susceptibility areas than other parts of the catchment. Almost 6.92 sq km (4.05%) and 7.44 sq km (4.36%) areas out of 170.61 sq km area of the basin have been observed as very high and high landslide susceptibility categories, respectively, for FR model and 5.75 sq km (3.37%) and 1.86 sq km (1.09%) areas have been under very high and high landslide susceptibility zones for LR model. Finally, the ROC curve has been used to validate the models. The prediction capabilities of the models seem significant as the area under the curve value ranges from 75 to 81%.

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