An ensemble landslide hazard model incorporating rainfall threshold for Mt. Umyeon, South Korea

In this study, a new ensemble method was developed to assess landslide hazard models in Mt. Umyeon, South Korea, using the results of a physically based model as a conditioning factor (CF). Hydrological conditions were obtained from the national-scale rainfall threshold. To incorporate rainfall threshold in landslide initiation, national landslide inventory data were used to prepare I-D and C-D thresholds. A series of factor of safety (FS) distribution maps were prepared using a physically based model with a 12-h cumulative rainfall threshold. We created an ensemble model to overcome limitations in the physically based model, which could not incorporate important environmental variables such as hydrology, forest, soil, and geology. To determine the effect of CFs on landslide distribution, spatial data layers of elevation, drainage proximity, soil drainage characters, stream power index, sediment transport index, topographic wetness index, forest type, forest density, tree diameter, soil type geology, and the FS distribution map were analyzed in a maximum entropy-based machine learning algorithm. Validation was performed with a receiver operating characteristic curve (ROC). The ROC showed 65.9% accuracy in the physically based model, whereas the ensemble model had higher accuracy (79.6%) and a prediction rate of 89.7%. The ensemble landslide hazard model is a new approach, incorporating the FS distribution map into the available independent environmental variables.