TY - JOUR ID - 86553 TI - Landslide Susceptibility assessment using GIS on Rock-Soil Slope along Zabidar Mountain Road Corridors, Ethiopia JO - Geopersia JA - GEOPE LA - en SN - 2228-7817 AU - HaileFekadu, Gashaw AU - Melese, Damtew Tsige AU - Woldesenbet, Tewodros Tsegaye AD - Wolkite University, Institute of Technology, Department of Civil Engineering, Tel: +251912377796, Wolkite, Ethiopia AD - Jimma University, Institute of Technology, Department of Civil Engineering, Tel: +251913969689, Jimma, Ethiopia AD - Jimma University, Institute of Technology, Department of Civil Engineering, Tel: +251912883113, Jimma, Ethiopia Y1 - 2022 PY - 2022 VL - 12 IS - 2 SP - 201 EP - 222 KW - causative factors KW - hazard map KW - Landslide KW - Slope DO - 10.22059/geope.2022.337838.648645 N2 - Landslides are deceitful natural disasters, resulting in the loss of human life, collapse of engineering structures, and the natural environment on the earth. Therefore, the aims of this study to assess, predict and mapping of susceptible landslide hazard map using GIS based software. Six landslide causative factors including aspect, distance from stream, lithology, plan curvature, slope and elevation selected as influencing factor for landslide occurrences. The landslide frequency ratio calculated using the probability technique. The controlling elements graded using a statistical and frequency ratio methodology based on GIS. The landslide hazard map shows 27% (4.8 km2) is no-danger zone, with 588 (41%) families living there. A medium to landslide danger zone covers 29% (5.2 km2), with 555 families (38.7%) living. A low-risk landslide zone covers 23% (4.1 km2), with 228 (16%) families living. A high-risk landslide zone covers 21% (3.8 km2), with 61 (4.3%) families living. The prediction rate of all factors revealed that, the highest landslide occurrence associated with Lithology and plan curvature. When these are added with high rainfall intensity, the magnitude of the landslide increases. The highest prediction accuracy of 89.58% found from combination of all causative factors which depicts how well the model and factors accurately forecast landslides. UR - https://geopersia.ut.ac.ir/article_86553.html L1 - https://geopersia.ut.ac.ir/article_86553_571e2bc4c44fbd2dcb73109f91ed6d6f.pdf ER -