Landslide Susceptibility Modeling Using the Index of Entropy and Frequency Ratio Method from Nefas-Mewcha to Weldiya Road Corridor, Northwestern Ethiopia

In Nefas-Mewcha to Weldiya road corridor (study area), landslide incidence resulted in the death of people, devastation of infrastructure, properties, crops, and agricultural lands. To reduce damages due to landslide incidences, a complete landslide susceptibility mapping was carried out using GIS-based index of entropy (IOE) and frequency ratio (FR) models. Detailed fieldwork and google earth imagery analysis were used to identify 712 landslides. These landslides were divided into two categories: 498 (70%) for modeling and 214 (30%) for model validation. The spatial relationship between pre-existing landslides and 12 landslide factors was performed. Using a raster calculator, the weighted landslide factors were combined to provide a landslide susceptibility index (LSI). The natural break classification method was used to divide the LSI into five categories: very low, low, moderate, high, and very high susceptibility zones. The area under the curve (AUC) and the receiver operating characteristic (ROC) curves were used to assess the models' performance and accuracy. The results showed that the IOE model (AUC = 70%) performed somewhat better than the FR model (AUC = 66.41%) in terms of prediction. The IOE method also showed slightly high model performance compared to FR with the success rate of AUC values (71.3% for IOE and 69% for FR). In the IOE model which was produced after selecting the landslide factors, the success rate showed an increment from 71.3% to 74.5%. Similarly, the FR model also showed significant change in a success rate of 78.1% and a predictive rate of 73.5%. According to this finding, the performance and predictability of landslide susceptibility mapping methods are influenced by landslide factors. Therefore, landslide factor optimization is a critical task before landslide susceptibility mapping.