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Title: Landslide Susceptibility Modeling Using Logistic Regression and Artificial Neural networks in GIS: a case study in Northern Showa area, Ethiopia.
Authors: Leta, Alemayehu
Advisors: Seyd Ali (PhD.)
Copyright: Feb-2008
Date Added: 15-Dec-2008
Publisher: Addis Ababa University
Abstract: Landslide susceptibility mapping has been undertaken using topographic parameters and lithologic information in north central Ethhiopia, Debresina area. The area has been one well known for landslide occurrences throughout its history. Before conducting the GIS analysis, extraction of some of the remote sensing data has been undertaken involving rectification and enhancement of Landsat ETM+ image of May 2000 for lithologic units interpretations. Image classification of 5/7, 5/4, ¾ band combination resulting in 70% accuracy and another classification using the first principal componets which accounted for 98 % of the whole bands data was done resulting in 69% accuracy. Further visual interpretation of the lithologic units has been undertaken to produce the final lithologic map interpretation. In addition the digital elevation model (DEM) of the area was obtained at 20m resolution by vectorizing contours from the topographic map of the area to extract the topographic parameters. In this study two susceptibility mapping methods have been employed: Logistic regression and Artificial Neural Network (ANN). Both have been used to generate the weights to represent the degree of contribution of selected seven parameters: Lithology, Slope, Aspect, Plan curvature, Profile curvature and flow accumulation in different platforms than geographic information system (GIS). Preceding to these the class weights of the various parameters were obtained by BSA method. Finally raster calculation of the seven layers of the parameters was conducted and two susceptibility maps were produced. The weights generated by ANN signified the contribution of Planar curvature, Aspect and Slope type towards landslide occurrence while that of the Logistic regression method signified Lithology and flow accumulation scoring higher in contribution towards landslide occurrence than the rest of the parameters. Finally, the outputs have been evaluated using the inventory of slope failure from the same period as those used for training the models and one from the recent massive landslide that occurred in the area.
Description: A thesis submitted to the school of graduate studies, Addis Ababa University In partial fulfillments for the degree of Master of Science in Remote Sensing and GIS.
URI: http://hdl.handle.net/123456789/1726
Appears in:Thesis - Earth Sciences
Thesis - Earth Sciences

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