Earthquakes, rainfall, or a combination of both can trigger landslides, which can be classified into shallow and deep-seated types according to scale. Landslide risk potential can be charted according to the spatiotemporal characteristics of a combination of triggering factors that can be collated for similar historical events by various methods. The geographic information system (GIS) and the instability index method are two approaches commonly used to perform such a task; however, the nature of the event and the quality of imported data affect the degree of bias of model predictions against real-time values. To identify the differences between shallow and deep-seated landslides, 324 cases of landslides triggered by single rainfall events in Taiwan are analyzed in this study. It is determined that the principal factor governing shallow failure for rainfall-induced landslides is slope and that deep-seated failure is controlled by the amount of accumulated rainfall. By arranging the weighting, these factors could predict 93% and 75% of the occurrences of shallow and deep-seated landslides, respectively, based on a pre-event digital terrain model.
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Engineering (miscellaneous)