Vol18 No.4: 1003-1012
【Title】Calculation of landslide occurrence probability in Taiwan region under different ground motion conditions
【Author】SHAO Xiao-yi1, XU Chong2,3,4*, MA Si-yuan1, XU Xi-wei2, J. BRUCE H. Shyu5, ZHOU Qing1
【Addresses】1 Key Laboratory of Seismic and Volcanic Hazards, Institute of Geology, China Earthquake Administration, Beijing 100029, China; 2 National Institute of Natural Hazards, Ministry of Emergency Management of China (former Institute of Crustal Dynamics, China Earthquake Administration), Beijing 100085, China; 3 Southern Yunnan Observatory for Cross-block Dynamic Process, Yuxi 652799, China; 4 Xichang Observatory for Natural Disaster Dynamics of Strike-slip Fault System in the Tibetan Plateau, Xichang 615000, China; 5 Department of Geosciences, National Taiwan University, Taipei 106, Chinese Taipei
【Corresponding author】XU Chong
【Citation】Shao XY, Xu C, Ma SY, et al. (2021) Calculation of landslide occurrence probability in Taiwan region under different ground motion conditions. Journal of Mountain Science 18(4). https://doi.org/10.1007/s11629-020-6540-2
【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.
【Keywords】Real occurrence probability; Earthquake induced landslide risk; Machine learning; Taiwan region