Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia)

Abstract

A landslide susceptibility mapping is essential for landslide hazard mitigation to reduce the associated risk. This paper aims to present the results of the landslide susceptibility modeling in the Citarik sub-watershed using three bivariate statistical-based methods, i.e., frequency ratio (FR), information value (IV), and weight of evidence (WoE). The main objective of this study is to evaluate the significance of the threshold of the area under curve (AUC) value in parameter selection. In this study, 118 landslide pixels were compiled from Google Earth images, unmanned aircraft vehicle (UAV) aerial photos taken just after the landslide, official landslide reports, and field observation. Thirteen landslide causative factors were prepared in Geographic Information System (GIS) environment, derived from various satellite images and maps. The landslide data were divided into two groups, 70% of data as training data and the rest as test data. Two scenarios that involve a different number of parameters were compared to explain the threshold of the AUC value in parameter selection and model accuracy. The result of this study shows that the AUC value threshold of 0.6 for parameter selection cannot be applied in all cases, and the performance of both two scenarios was excellent in assessing landslide susceptibility in this study area. Those three landslide susceptibility zonation maps of the best scenario showed that the sub-watershed's northern, northeastern, south-eastern, and southern parts were under high to very high susceptibility to landslides, including the Cimanggung area where a recent deadly double landslide occurred.