Landslide Susceptibility Mapping Using Machine Learning Algorithms and Prioritization of Factors Affecting the Occurrence of Landslides

The aim of this study was to model the landslide susceptibility using the Random Forest Machine learning technique and prioritization of effective factors on landslide occurrence in Bar watershed in Khorasan Razavi province. The random forest algorithm is based on a bunch of decision trees and is currently one of the best machine learning algorithms. For this purpose, a landslide inventory map was created with 73 historical landslides, which was randomly divided into two datasets for model training (70%) and model testing (30%). A total of 16 landslide-conditioning factors were considered for the susceptibility landslide mapping. The random forest algorithm was run and a landslide susceptibility map was prepared. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. The results of evaluation indicated that the success and prediction rates of the model were 90% in training and 89% in validation, respectively. These results confirm the ability of random forest method for prediction of landslide susceptibility models. Also, prioritization of the effective factors showed that the slope length and slope had the highest effect on landslide occurrence. Based on the results of the random forest model, 23.7% of the study area is located in a very high and high sensitivity zone.