Landslide Hazard Zonation using Logistic Regression Model: The Case of Shafe and Baso Catchments, Gamo Highland, Southern Ethiopia

Author(s):  
Leulalem Shano ◽  
Tarun Kumar Raghuvanshi ◽  
Matebie Meten
2021 ◽  
Author(s):  
Leulalem Shano ◽  
Tarun Kumar Raghuvanshi ◽  
Matebie Meten

Abstract Landslide hazard zonation plays an important role in safe and viable infrastructure development, urbanization, land use, and environmental planning. The Shafe and Baso catchments are found in the Gamo highland which has been highly degraded by erosion and landslides thereby affecting the lives of the local people. In recent decades, recurrent landslide incidences were frequently occurring in this Highland region of Ethiopia in almost every rainy season. This demands landslide hazard zonation in the study area in order to alleviate the problems associated with these landslides. The main objectives of this study are to identify the spatiotemporal landslide distribution of the area; evaluate the landslide influencing factors and prepare the landslide hazard map. In the present study, lithology, groundwater conditions, distance to faults, morphometric factors (slope, aspect and curvature), and land use/land cover were considered as landslide predisposing/influencing factors while precipitation was a triggering factor. All these factor maps and landslide inventory maps were integrated using ArcGIS 10.4 environment. For data analysis, the principle of logistic regression was applied in a statistical package for social sciences (SPSS). The result from this statistical analysis showed that the landslide influencing factors like distance to fault, distance to stream, groundwater zones, lithological units and aspect have revealed the highest contribution to landslide occurrence as they showed greater than a unit odds ratio. The resulting landslide hazard map was divided into five classes: very low (13.48%), low (28.67%), moderate (31.62%), high (18%), and very high (8.2%) hazard zones which was then validated using the goodness of fit techniques and receiver operating characteristic curve (ROC) with an accuracy of 85.4. The high and very high landslide hazard zones should be avoided from further infrastructure and settlement planning unless proper and cost-effective landslide mitigation measures are implemented.


2020 ◽  
Author(s):  
Abdurohman Adem ◽  
Suryabhagavan Venkata Karuturi ◽  
Tarun Kumar Raghuvanshi

Abstract The present study was undertaken to identify landslides hazard prone areas in North Ethiopia. The landslide hazard in the present study area was evaluated by using the logistic regression model. Seven landslide causative factors were used for the landslide hazard evaluation, these are; slope gradient, slope aspect, elevation, proximity to streams, land-use/ land-cover, lithology and Normalized Difference Vegetation Index. Besides, for the present study landslides inventory data for the period of 2000 to 2018 was collected from the field survey and the Google earth image interpretation. The coefficient for the considered causative factors and classes were used for the identification of landslides hazard index using raster tool in ARCGIS environment. The prediction of the logistic regression model reveals that one third of the study area (32%) is under high hazard zone and the steep slopes and the elevated areas are most susceptible areas. The predicted landslides hazard zonation map is highly correlated with the training data set where 74% of it lies in the very high and high landslide hazard zones. Results of the area under the Receiver Operating Characteristic curve for the training sample, was found to be 0.76 while the area under the ROC curve of the validation sample was 0.71. Thus, the validation results has confirmed the rationality of adopted methodology, considered causative factors and their evaluation in producing LHZ map for the area. Further, the study has forwarded recommendations that can be followed to prevent and mitigate the adverse impact of landslides in the study area.


2014 ◽  
Vol 47 (5) ◽  
pp. 565-589 ◽  
Author(s):  
Saro Lee ◽  
Joong-Sun Won ◽  
Seong Woo Jeon ◽  
Inhye Park ◽  
Moung Jin Lee

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