Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

Ahmed Cemiloglu, Licai Zhu, Agab Bakheet Mohammednour, Mohammad Azarafza and Yaser Ahangari Nanehkaran ()
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Ahmed Cemiloglu: School of Information Engineering, Yancheng Teachers University, Yancheng 224002, China
Licai Zhu: School of Information Engineering, Yancheng Teachers University, Yancheng 224002, China
Agab Bakheet Mohammednour: Department of Control System Engineering, Al-Neelain University, Khartoum 12702, Sudan
Mohammad Azarafza: Geotechnical Department, Faculty of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran
Yaser Ahangari Nanehkaran: School of Information Engineering, Yancheng Teachers University, Yancheng 224002, China

Land, 2023, vol. 12, issue 7, 1-20

Abstract: Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistical-based model that investigates the probabilities of the events which is received extensive success in landslide susceptibility assessment. The presented study attempted to use a logistic regression application to prepare the Maragheh County hazard risk map. In this regard, several predisposing factors (e.g., elevation, slope aspect, slope angle, rainfall, land use, lithology, weathering, distance from faults, distance from the river, distance from the road, and distance from cities) are identified as main responsible for landslide occurrence and 20 historical sliding events which used to prepare hazard risk maps. As verification, the models were controlled by operating relative characteristics (ROC) curves which reported the overall accuracy for susceptibility assessment. According to the results, the region is located in a moderate to high-hazard risk zone. The north and northeast parts of Maragheh County show high suitability for landslides. Verification results of the model indicated that the AUC estimated for the training set is 0.885, and the AUC estimated for the testing set is 0.769. To justify the model, the results of the LR were comparatively checked with several benchmark learning models. Results indicated that LR model performance is reasonable.

Keywords: landslides; susceptibility analysis; logistic regression; hazard mapping; geo-hazards (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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