NDLI: GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia

Content Provider	Springer Nature Link
Author	Meten, Matebie Bhandary, Netra Prakash Yatabe, Ryuichi
Copyright Year	2015
Abstract	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, GIS-based 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.
Starting Page	1355
Ending Page	1372
Page Count	18
File Format	PDF
ISSN	16726316
Journal	Journal of Mountain Science
Volume Number	12
Issue Number	6
e-ISSN	19930321
Language	English
Publisher	Science Press
Publisher Date	2015-11-19
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Landslide susceptibility GIS Frequency Ratio Logistic Regression Debre Sina Ethiopia Earth Sciences Geography Environment Ecology
Content Type	Text
Resource Type	Article
Subject	Earth-Surface Processes Geography, Planning and Development Geology Global and Planetary Change Nature and Landscape Conservation

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