GIS-based Frequency Ratio and Logistic Regression Modelling for Landslide Susceptibility Mapping of Debre Sina Area in Central Ethiopia

作     者：Matebie METEN Netra Prakash BHANDARY Ryuichi YATABE 

作者机构：Geo-Disaster Research Laboratory Graduate School of Science and Engineering Ehime University 

出 版 物：《Journal of Mountain Science》 (山地科学学报（英文）)

年 卷 期：2015年第12卷第6期

页      面：1355-1372页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 081803[工学-地质工程] 07[理学] 08[工学] 0818[工学-地质资源与地质工程] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：Japanese Government for Scholarship through Ministry of Education  culture  Science & Technology (MEXT) 

主　　题：Landslide susceptibility GIS Frequency Ratio Logistic Regression Debre Sina Ethiopia 

摘      要：Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.