Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershade, Ethiopia

Résumé

Abstract In this study, landslide susceptibility mapping was carried out for Chemoga watershade upper Abay basin in Ethiopia using a bivariate statistical models of Frequency Ratio (FR) and Information Values (IV). The main objective of this study is to identified landslide susceptibility areas using GIS and Bivariate Statistical Models. Based on Google Earth imagery and filed survey, about 169 landslides were identified and classified randomly into training landslides datasets (70%) and the remaining (30%) of landslides datasets were used for validation purpose. The 10 landslides conditioning factors like slope, elevation, aspect, curvature, Topographic Wetness Index, Normalized Difference Vegetation Index, road, river, land use and rainfall were integrated with training landslides to determine the weights of each landslide conditioning factor and factor classes using both frequency ratio and information value models. The landslide susceptibility maps were produced by overlay the weights of all the landslide conditioning factors using raster calculator of the spatial analyst tool in ArcGIS 10.4. The final landslide susceptibility maps were reclassified as very low, low, moderate, high and very high susceptibility classes both FR and IV models.This susceptibility maps were validated using landslide area under the curve (AUC). The results of AUC accuracy models showed that the success rates of the FR and IV models were 0.870 and 0.901, while the prediction rates were 0.880 and 0.923 respectively. The outcome of this paper will help decision-makers for land use planning and landslide mitigation purpose in this study area.

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