Abstract
AbstractThe study area in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent high landslide occurrences. To predict future landslide occurrence, preparing a landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. Geographic information system (GIS)-based frequency ratio (FR), information value (IV), certainty factor (CF), and logistic regression (LR) methods were applied. The landslide inventory map is prepared from historical records and Google Earth imagery interpretation. Thus, 717 landslides were mapped, of which 502 (70%) landslides were used to build landslide susceptibility models, and the remaining 215 (30%) landslides were used to model validation. Eleven factors such as lithology, land use/cover, distance to drainage, distance to lineament, normalized difference vegetation index, drainage density, rainfall, soil type, slope, aspect, and curvature were evaluated and their relationship with landslide occurrence was analyzed using the GIS tool. Then, landslide susceptibility maps of the study area are categorized into very low, low, moderate, high, and very high susceptibility classes. The four models were validated by the area under the curve (AUC) and landslide density. The results for the AUC are 93.9% for the CF model, which is better than 93.2% using IV, 92.7% using the FR model, and 87.9% using the LR model. Moreover, the statistical significance test between the models was performed using LR analysis by SPSS software. The result showed that the LR and CF models have higher statistical significance than the FR and IV methods. Although all statistical models indicated higher prediction accuracy, based on their statistical significance analysis result (Table 5), the LR model is relatively better followed by the CF model for regional land use planning, landslide hazard mitigation, and prevention purposes.
Highlights
As defined by Brunsden and Cruden [1,2], landslides are the downslope movements of debris, rocks, or earth material under the influence of the force of gravity [3]
The higher value of the frequency ratio (FR), information value (IV), and certainty factor (CF) indicated the strong correlation between the landslide and landslide factor classes
The area under the curve (AUC) values of all methods except logistic regression (LR) fell in the same range of excellent performance. These results indicated that the FR, CF, IV, and LR models have successfully estimated the landslide susceptibility classes of the region, and these models, which were used in this study, have reasonable accuracy in predicting the landslide susceptibility classes of the study area
Summary
As defined by Brunsden and Cruden [1,2], landslides are the downslope movements of debris, rocks, or earth material under the influence of the force of gravity [3]. The cause of landslide incidence and its mechanisms are so complex, it is triggered by heavy rainfall, earthquake, and human interventions It can occur when the driving force exceeds the resistance force because of the destabilization of natural soil or rock slopes [4,5]. Landslides can bury animals and humans in a short period as well as can destroy houses, farms, and infrastructures [6] It is one of the most destructive and dangerous natural hazards that cause numerous fatality and economic losses worldwide [6,7,8,9]. Ethiopia is one of the countries affected by heavy rainfall, human intervention, and earthquake-triggered frequent landslide impacts yearly, resulting in loss of human and animal lives and damage of infrastructures and properties [5,10,11,12,13,14]. The study area is one of the areas that were frequently affected
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