A replication of landslide hazard mapping at catchment scale

Abstract

Landslide hazard assessment is a primary tool to understand the basic characteristics of slopes that are prone to landslides, especially during extreme rainfall. In this study, weights-of-evidence modelling a bivariate statistical method, and a logistic regression model, a multivariate statistical method, were used for landslide hazard mapping in two catchments of the Siwaliks in the Nepal Himalaya. Two typical catchments, Charnath and Jalad of the Siwaliks in easternNepal, were selected for the landslide hazard mapping. Both modelling approaches were applied by considering 10 intrinsic factors and one extrinsic factor. Mainly digital elevation model-based causative factors and field data were used to prepare data layers of landslide causative factors. In many approaches formodelling of landslide hazard in GIS, the model validation process is always dependent, and landslide data, which are used to calculate a landslide hazard index (LHI), are applied for verification. However, in this study, the LHI was calculated in one catchment (Jalad) and the same index for a different class of causative factors was applied for another catchment (Charnath), and the LHI wasverified. The verification results were very promising, with an independent prediction rate of about 75%. This validates weights-of-evidence and logistic regression models for landslide hazard assessment in the Siwaliks Range of Nepal.