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.

Spatial Analysis of Causative Factors for Landslide Susceptibility on Java Island

“…Slope was classified into seven classes using the Van Zuidam classification [10]. Meanwhile, aspect was classified into nine classes [11]. The parameters distance to fault, PGA, rainfall, elevation, distance to river, and river density were classified using the quantile classification method into ten classes.…”

“…FR method represents the probability of landslide occurrence in each parameter class. The greater the FR value of a parameter class, the greater the probability of landslide occurrence in that parameter class [11]. The FR value can be calculated based on equation (1).…”

“…Landslide susceptibility map is represented in the form of index map, namely Landslide Susceptibility Index (LSI), which is obtained from the sum of FR values of all parameters causing landslides [11]. The LSI value can be calculated based on equation (2).…”

“…Receiver Operating Characteristic (ROC) curve is used to validate the landslide susceptibility map because ROC curve can verify the model performance by showing the success and predictive rate of the model [12]. ROC curve shows the false positive value on the X-axis and the true positive value on the Y-axis in a graphical plot [11]. False positive is defined as the number of pixels or areas that are falsely classified as landslides.…”

Modeling and mapping the environmental impact of debris flow hazard on alluvial fans for sustainable development in Bangga and Poi Villages, Sigi, Central Sulawesi

“…Landslide inventories are required as reference data for studying, evaluating, and mapping the hazards associated with landslides [4,5]. The landslides caused by an earthquake are also known as coseismic landslides or earthquake-triggered landslides (EQTLs) [6].…”