An Assessment of Data Mining and Bivariate Statistical Methods for Landslide Susceptibility Mapping

Abstract

Landslides are recognized as one of the environmental challenges that lead to land degradation, reduce fertility, and cause significant damage to the ecosystem. Therefore, proper identification of landslide-prone areas through modeling will be significantly helpful for land development managers and planners by providing them with appropriate management strategies to prevent land degradation. In this research, landslide susceptibility mapping was carried out for West Azerbaijan province in Iran using Frequency Ratio (FR), Shannon Entropy (SE), Random Forest (RF), and an ensemble of random forest and bagging (RF-BA) methods. Based on field surveys, local interviews, and review of similar studies, 12 factors influencing landslide occurrence, namely altitude, slope angle, slope aspect, distance from fault, distance from river, distance from road, drainage density, road density, rainfall, soil, land use, and lithology, were identified. In the field surveys, 110 landslides in the area were specified; 70% of the data (77 landslides) were randomly selected and utilized for modeling and the remaining 30% (33 landslides) for validation. The results of the ROC curve showed the accuracy of 0.92, 0.91, 0.89, and 0.88 with the RF-BA, RF, FR, and SE models, respectively.