Abstract
Abstract Correb area in Mekdela Woreda, South-Wollo Zone, North-Western Ethiopia is one of the most landslide affected region. The primary aim of this study was to assess the landslide susceptibility of the area and create a landslide susceptibility map. A bivariate statistical frequency ratio model was used to accomplish this goal. Using fieldwork and Google Earth picture interpretation, a map of landslides was made. A total of 524 landslides were identified and then classified in to training landslide (75%) for model development and validation landslide (25%) for model validation. Eight landslide causative factors slope, aspect, elevation, distance to drainage, distance to spring, slope material, distance to lineament, and vegetation cover were integrated with training landslides in order to determine frequency ratio value(weight) for each class of landslide causative factors. Furthermore, each causative factor’s prediction rate or weight was established. Relative frequency values were allocated to the appropriate factor classes and prediction rate values were allocated to the appropriate causative factors and summed using a raster calculator algorithm in order to produce the landslide susceptibility map. The final landslide susceptibility map revealed that 27.54% (31.52 km2) of the study area falls under very low susceptible, 30.35% (34.74 km2) low susceptible, 21.36% (24.44 km2) moderate susceptible, 14.74% (16.88 km2) high susceptible, and the rest 6.01% (6.88 km2) as very high susceptible. This shows that more than 20% of the area has probability and risk of landslide while about 80% has relatively low probability and risk of this natural hazard. The performance of modified frequency ratio model for landslide susceptibility mapping was validated using simply overlay, relative landslide density index, and receiver operating characteristics curve. Both training and validation landslides were rare in the very low and low susceptibility classes and strongly concentrated in the high and very high susceptibility classes, according to validation using simple overlay and the relative landslide density index. The implemented model performed exceptionally well, as seen by the receiver operating characteristics curve, which showed 82.6% success rate and an 83.1% prediction rate. In general, the model produced reasonable accuracy. The resultant map would be useful for general land use planning, site selection, and landslide prevention and mitigation programs.