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Concept on Landslides and Landslide Susceptibility

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Statistical Approaches for Landslide Susceptibility Assessment and Prediction

Abstract

Landslide is one of the destructive environmental hazards which causes a lot of damages to human lives and properties. Various approaches and techniques have been applied to assess the spatial distribution of landslides all over the world. Amongst them physical models, slope stability models, statistical and probabilistic models are very much important in the study of landslide assessment and prediction. In the present study, to assess the spatial distribution of landslide susceptibility in Darjeeling Himalaya several statistical models, i.e. frequency ratio (FR) model, modified information value (MIV) model, logistic regression (LR) model, artificial neural network (ANN) model, weighted overlay analysis (WOA) model, certainty factor (CF) model, analytical hierarchy process (AHP) model and fuzzy logic (FL) approach have been incorporated and finally a comparison has been made between the models on the basis of model validation results. Physical models with regard to landslides dealt with the assessment of various physical parameters of rocks and soil, i.e. shear stress, shear strength, cohesion, friction angle, pore-water pressure, grain size of soil, depth of the soil, saturated soil depth, density of water and soil, etc. All these parameters help to perform slope stability model as well as to identify the vulnerable slope in the mountain environment. In Darjeeling Himalaya, debris slide, rock fall, and earth slides are three common types of landslides. Statistical models based on RS and GIS help to identify susceptible landslide locations with accuracy. To perform all the statistical models, a landslide inventory was made based on historical landslides data, toposheet, Google earth image, and field investigation with GPS survey. The data layers, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were integrated on GIS platform to assess pixel wise landslide susceptibility index, and finally a classification was made to prepare landslide susceptibility zonation map of Darjeeling Himalaya in connection to each model.

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Authors and Affiliations

  1. Department of Geography, Diamond Harbour Women’s University, Sarisha, West Bengal, India

    Sujit Mandal

  2. University of Gour Banga, Mokdumpur, West Bengal, India

    Subrata Mondal

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Mandal, S., Mondal, S. (2019). Concept on Landslides and Landslide Susceptibility. In: Statistical Approaches for Landslide Susceptibility Assessment and Prediction. Springer, Cham. https://doi.org/10.1007/978-3-319-93897-4_1

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