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Dastranj A, Noor H, Bagherian Kalat A. GIS-based Landslide Susceptibility Zoning Using Multi-Criteria Decision-making Method: A Case Study in Binalood Mountains, Iran. jorar 2022; 14 (1) :19-29
URL: http://jorar.ir/article-1-726-fa.html

INTRODUCTION: Landslides are one of the recurrent natural problems that are widespread throughout the world, especially in mountainous areas, and cause a significant injury to and loss of human life and damage to properties and infrastructures. This study aimed to assess landslide susceptibility using the analytic hierarchy process (AHP) in Binalood Mountains, Razavi Khorasan Province, Iran.
METHODS: Since the Binalood Mountains range has a high potential for landslides occurrence, the present study went through to map landslide susceptibility. To accomplish this, the AHP method was used, and then, receiver operating characteristic/area under the curves (AUCs) were prepared to evaluate the performance of the susceptibility map. Multiple data, such as lithology, distance to faults, land use, distance to roads, altitude, slope, aspect, stream power index, topographic wetness index, rainfall, distance from rivers, slope length index, and topographic location index, were considered for delineating the landslide susceptibility maps. These thematic layers were assigned suitable weights on the Saaty's scale according to their relative importance in landslide occurrence in the study area. The assigned weights of the thematic layers and their features were subsequently normalized using the AHP technique. Finally, all thematic layers were integrated by a weighted linear combination method in a geographic information system tool to generate landslide susceptibility maps.
FINDINGS: The landslide susceptibility maps are split into five classes, namely very low, low, moderate, high, and very high. The results showed that the geological factor was the most important factor affecting the occurrence of landslides in the study area. Generally, 47.8% of the total area was considered high and very high-risk areas. The prediction accuracy of this map showed the values of AUC equal to 81.7% that showed the AHP model had very good accuracy.
CONCLUSION: Overall, AHP is acceptable for landslide susceptibility mapping in the study area. A landslide susceptibility map is a useful tool to help with land management in landslide-prone areas. The results revealed that the predicted susceptibility levels were found to be in good agreement with the past landslide occurrences. Possibly, this map can be used by the concerned authorities in disaster management planning to prepare rescue routes, service centers, and shelters.