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 C...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.展开更多
With the advancement of the global economy,the coastal region has become heavily developed and densely populated and suffers significant damage potential considering various natural disasters,including tsunamis,as ind...With the advancement of the global economy,the coastal region has become heavily developed and densely populated and suffers significant damage potential considering various natural disasters,including tsunamis,as indicated by several catastrophic tsunami disasters in the 21st century.This study reviews the up-to-date tsunami research from two different viewpoints:tsunamis caused by different generation mechanisms and tsunami research applying different research approaches.For the first issue,earthquake-induced,landslide-induced,volcano eruption-induced,and meteorological tsunamis are individually reviewed,and the characteristics of each tsunami research are specified.Regarding the second issue,tsunami research using post-tsunami field surveys,numerical simulations,and laboratory experiments are discussed individually.Research outcomes from each approach are then summarized.With the extending and deepening of the understanding of tsunamis and their inherent physical insights,highly effective and precise tsunami early warning systems and countermeasures are expected for the relevant disaster protection and mitigation efforts in the coastal region.展开更多
基金supported by the National Key Research and Development Program of China(2018YFC1504703)。
文摘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.
基金the National Natural Science Foundation of China under Grant Nos.52271292,52071288the Science and Technology Innovation 2025 Major Project of Ningbo City under Grant No.2022Z213.
文摘With the advancement of the global economy,the coastal region has become heavily developed and densely populated and suffers significant damage potential considering various natural disasters,including tsunamis,as indicated by several catastrophic tsunami disasters in the 21st century.This study reviews the up-to-date tsunami research from two different viewpoints:tsunamis caused by different generation mechanisms and tsunami research applying different research approaches.For the first issue,earthquake-induced,landslide-induced,volcano eruption-induced,and meteorological tsunamis are individually reviewed,and the characteristics of each tsunami research are specified.Regarding the second issue,tsunami research using post-tsunami field surveys,numerical simulations,and laboratory experiments are discussed individually.Research outcomes from each approach are then summarized.With the extending and deepening of the understanding of tsunamis and their inherent physical insights,highly effective and precise tsunami early warning systems and countermeasures are expected for the relevant disaster protection and mitigation efforts in the coastal region.
