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Frequency Ratio (FR) Model And Modified Information Value (MIV) Model In Landslide Susceptibility Assessment And Prediction

Sujit Mandal, Subrata Mondal
Published 2019 · Geology

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The assessment of landslide susceptibility is closely associated with the spatial distribution of landslides. In the present study, both frequency ratio (FR) model and modified information value (MIV) model were applied to analyse landslide susceptibility in Darjeeling Himalaya. Both the models dealt with the relationship between landslide phenomena and landslide conditioning factors. To perform the models 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 taken into account. Each and every class/category of landslide conditioning factor contributes a relative importance in landslide occurrences. To prepare all the data layers, Landsat TM image, SRTM DEM, Google earth image, and some authorized maps were processed in accordance with ArcMap 10.1 and Erdas imagine 9.2. To obtain the relative significance of each class/category of landslide conditioning factors, frequency ratio (FR) value and modified information value (MIV) were estimated and accordingly the ranking values were assigned to each class/category to integrate all the data layers on GIS platform as well as to prepare landslide susceptibility map of Darjeeling Himalaya. The derived landslides susceptibility maps by using frequency ratio model and modified information value model were verified being considering the area under curve (AUC) of ROC curve and frequency ratio plot. The AUC value of ROC curve of FR model and MIV model was 0.746 and 0.769, respectively. The AUC value represents the prediction accuracy of landslide susceptibility map was 74.6% for frequency ratio model and 76.9% for modified information value model.
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