| Abstract: | In this study, Bayesian probability method and machine learning model are used to study the real occurrence probability of earthquake-induced landslide risk in Taiwan region. The analyses were based on the 1999 Taiwan Chi-Chi Earthquake, the largest earthquake in the history in this Region in a hundred years, thus can provide better control on the prediction accuracy of the model. This seismic event has detailed and complete seismic landslide inventories identified by polygons, including 9272 seismic landslide records. Taking into account the real earthquake landslide occurrence area, the difference in landslide area and the non-sliding/sliding sample ratios and other factors, a total of 13,656,000 model training samples were selected. We also considered other seismic landslide influencing factors, including elevation, slope, aspect, topographic wetness index, lithology, distance to fault, peak ground acceleration and rainfall. Bayesian probability method and machine learning model were combined to establish the multi-factor influence of earthquake landslide occurrence model. The model is then applied to the whole Taiwan region using different ground motion peak accelerations(from 0.1 g to 1.0 g with 0.1 g intervals) as a triggering factor to complete the real probability of earthquake landslide map in Taiwan under different peak ground accelerations, and the functional relationship between different Peak Ground Acceleration and their predicted area is obtained. |
