Landslide susceptibility mapping using frequency ratio, logistic regression and AHP models in the part of Golestan province

Subject Areas : Sustainable Development

Majid Mohammady ¹ , Shantia Jamal ² , Arman Mansoori ³

1 - Assistant Prof., Faculty of Natural Resources, Semnan University, Semnan, Iran.*(Corresponding Author)
2 - Faculty of Natural Resources, Semnan University, Semnan, Iran.
3 - Faculty of Natural Resources, Semnan University, Semnan, Iran.

Received: 2016-06-20 Accepted : 2016-12-28 Published : 2021-11-22

Keywords: logistic regression, Analytical hierarchy process, Landslide, Frequency ratio,

Abstract :

Background and objective: Iran is always exposed to landslide hazard especially in the north because of climatic and topographic conditions. Identification of landslide prone areas and its hazards is one of the first works in natural resources management and development programs. Policymakers pay high attention to landslide investigation in order to landslide susceptibility mapping and identifying susceptible areas and stable locations for development of new settlements in the future. The main goal of his research is landslide susceptibility mapping in the part of Golestan province.Material and Methodology: 78 landslides were identified from the field surveys in the part of Golestan province, and then landslide inventory map was created in year 2016. Effective factor maps such as slope degree, slope aspect, plan curvature, altitude, lithology, land use, distance from road, distance from fault, distance from drainage and slope-length (LS), were prepared in the GIS environment. Three methods such as analytical hierarchy process, frequency ratio and logistic ratio were applied to landslide susceptibility mapping. Also ROC curve was used to accuracy assessment of susceptibility mapsFindings: prioritization of effective factors using AHP showed that distance from road, slope, distance from drainage and distance from residential area have the most effect on landslide occurrence. Landslide susceptibility map obtained from three models was compared using Relative Operating Characteristic (ROC) and Area under Curve (AUC). The result showed that frequency ratio model with the AUC equal to 0.8 has the most accuracy to landslide susceptibility mapping.Discussion and Conclusion: In general, the results showed that the study area has a high potential for landslides occurrence. Identifying susceptible areas help to prevent changes in the natural state of these areas as much as possible.

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