Document Type : Original Article

Authors

• Javad Sadidi ¹
• Reza Maliki ²

¹ PhD, Associate Professor, Educational Department of Remote Sensing and Geographical Information System, Faculty of Geographical Sciences, Khwarazmi University

² M.Sc student , Faculty of Geographical Sciences, Kharazmi University

Abstract

In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.

Keywords

• Landslide
• Machine learning
• Random forest
• Support vector machine
• Mahabad-Sardasht