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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. [ABSTRACT FROM AUTHOR]

In Nepal, people live in widely spread settlements in the fragile Himalayan terrains, and suffer more from landslides than from any other type of natural disaster. The small-scale rainfall-triggered landslides in the Lesser Himalaya of Nepal are generally shallow (about 0.5 to 2.5 m) and are triggered by changes in the physical property of soil layers during rainfall. The relation between landslides and slope hydrology has received little attention in Himalayan landslide research. Thus, this paper deals with the probability of slope failure during extreme rainfall events by considering a digital elevation model (DEM)-based hydrological model for soil saturation depth and an infinite slope stability model. Deterministic distributed analysis in a geographic information system (GIS) was carried out to calculate the probability of slope failure. A simple method of error propagation was used to calculate the variance of the safety factors and the probability of failure. When normally distributed failure probability values were checked against existing landslides, it was found that more than 50% of the pixels of existing landslides coincided with a high calculated probability of failure. Although the deterministic distributed analysis has certain drawbacks, as described by previous researchers, this study concluded that the calculated failure probability can be utilised to predict the probability of slope failure in Himalayan terrain during extreme rainfall events. [ABSTRACT FROM AUTHOR]

Abstract: Landslide hazard mapping is a fundamental tool for disaster management activities in mountainous terrains. The main purpose of this study is to evaluate the predictive power of weights-of-evidence modelling in landslide hazard assessment in the Lesser Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the south-western marginal hills of the Kathmandu Valley. Thematic maps representing various factors (e.g., slope, aspect, relief, flow accumulation, distance to drainage, soil depth, engineering soil type, landuse, geology, distance to road and extreme one-day rainfall) that are related to landslide activity were generated, using field data and GIS techniques, at a scale of 1:10,000. Landslide events of the 1970s, 1980s, and 1990s were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. To assess the accuracy of the resulting landslide hazard map, it was correlated with a map of landslides triggered by the 2002 extreme rainfall events. The accuracy of the map was evaluated by various techniques, including the area under the curve, success rate and prediction rate. The resulting landslide hazard value calculated from the old landslide data showed a prediction accuracy of >80%. The analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value, while geological factors play only minor roles. Finally, after the rectification of the landslide hazard values of the new landslides using those of the old landslides, a landslide hazard map with >88% prediction accuracy was prepared. The methodology appears to have extensive applicability to the Lesser Himalaya of Nepal, with the limitation that the model''s performance is contingent on the availability of data from past landslides. [Copyright &y& Elsevier]