Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon, Seoul, Korea

Abstract

Predictive mapping of landslide occurrence at the regional scale was performed at Mt. Umyeon, in the southern part of Seoul, Korea, using an evidential belief function (EBF) model. To generate the landslide susceptibility map, approximately 90 % of 163 actual landslide locations were randomly selected as a training set, and about 10 % of them were used as a validation set. Spatial data sets relevant to landslide occurrence (topographic factors, hydrologic factors, forest factors, soil factors, and geologic factors) were analyzed in a geographic information system environment. In this study, landslide susceptibility was assessed on the basis of mass function assignment (belief, disbelief, uncertainty, and plausibility) and integration within a data-driven approach. The most representative of the resulting integrated susceptibility maps (the belief map) was validated using the receiver operating characteristic (ROC) method. The verification result showed that the model had an accuracy of 74.3 % and a predictive accuracy of 88.1 %. The frequency ratio (FR) model was also used for comparison with the EBF model. Prediction and success rates of 72.1 and 85.9 % were achieved using the FR model. The validation results showed satisfactory agreement between the susceptibility map and the existing landslide location data. The EBF model was more accurate than the FR model for landslide prediction in the study area. The results of this study can be used to mitigate landslide-induced hazards and for land-use planning.