Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India)

The purpose of this study is to develop a landslide susceptibility prediction model by applying the Frequency Ratio (FR) model and remote sensing data sets for the Northern part of Uttarakhand, India. First, a landslide inventory was carried out from the interpretation of satellite images. Thereafter, the landslide inventory points were randomly separated into training and validation datasets. Subsequently, the significant landslide causative factors such as slope, lithology, lineament density, land use/land cover, drainage density, aspect, elevation, road buffer, normalized differential vegetation index (NDVI), stream power index, and topographic wetness index were identified to run the model set up. Next, applying the FR statistical model in a GIS environment for development of landslide susceptibility index map and divided into five distinct landslide susceptibility zones (very low, low, moderate, high, and very high). To validate the results, the Receiver Operating Characteristics (ROC) curve were developed to check the accurrancy of the model, and it was observed that the prediction value of the FR model was reasonably accurate (86.1% at 95% confidence level). The output LSI map would be helpful for the government and planners to map and monitor potential landslide areas and mitigate the hazards.