Spatial prediction of landslide susceptibility using the data-mining algorithm (case study: Kamyaran county)

Introduction Mass movements, such as landslides on slopes, are a type of slope activity and a category of natural hazards that result in significant financial, human, and environmental damages globally each year. Identification and classification of regions susceptible to landslides are crucial components of environmental risk evaluation and play a significant role in watershed management. Methods The aim of this study is to assess the spatial susceptibility of landslides utilizing sophisticated data mining techniques in the Kamyaran County, Iran. Accordingly, the evaluation of landslide susceptibility was carried out employing two advanced data mining approaches, namely, Random Forest and Support Vector Machine. In this research, the variables considered for hazard potential zoning included elevation, slope, aspect, slope curvature, distance from rivers, distance from roads, distance from faults, land use, normalized difference vegetation index, lithology, rainfall, and topographic wetness index. A dataset of landslides was utilized for this purpose. The dataset included 103 recorded landslides in Kamyaran County, which served as a map for the actual landslides that took place in the area. To train and validate the models, the landslide data points were split into two subsets, namely, training data (70 percent), consisting of 72 points, and validation data (30 percent), comprising 31 points. Ultimately, the efficacy of the models was assessed using the receiver operating characteristic (ROC) curve. Results The findings from the ROC curve analysis revealed that the SVM and RF models achieved AUC values of 0.91 and 0.95, respectively; thus, the RF model exhibited the highest AUC value in comparison to the SVM, making it the most effective model for forecasting landslide susceptibility in the study area in the future. Conclusion Landslide potential maps are valuable tools that can be applied in environmental management, land use planning, and infrastructure development.