摘要： Many shallow landslides in Taiwan are triggered by typhoons (tropical cyclones) during the summer months. Each typhoon produces a different rainfall distribution, depending on its track and position and the atmospheric conditions. This study investigated whether the additional rainfall data in a landslide susceptibility model can improve its performance in predicting typhoon-triggered landslides, and whether information on past typhoon events, combined with an event-based landslide inventory, can help predict landslides triggered by a typhoon. To answer these questions, the study adopted a method that integrates rainfall data with the critical rainfall model (a landslide susceptibility model based on geoenvironmental factors) to derive a logistic regression model for predicting landslide occurrence. Results of a back analysis of landslides triggered by nine typhoons from 2001 to 2009 reveal that, by including rainfall data, the integrated method performs better than the critical rainfall model in the average overall accuracy rate (0.78 vs. 0.45) and the average modified success rate (0.75 vs. 0.68). Our preliminary results also suggest that it is possible to predict landslides triggered by a typhoon by using a catch-all model developed from all other typhoon events in an inventory, or a group model developed from other typhoon events of similar rainfall characteristics in an inventory. This study opens up a new research direction in analyzing rainfall-triggered landslides in Taiwan and elsewhere. •Additional rainfall data can improve a landslide susceptibility model’s performance.•The integrated model performs better than the critical rainfall model and is transferable.•Information on past typhoon events can help predict landslides triggered by a typhoon.•Information on past typhoon events can be included in catch-all or group models. 出版者： Amsterdam: Elsevier B.V 出版日期： 2014-03-01 出處： Geomorphology, 2014-03, Vol.208, p.137-148 資源來源： Elsevier ScienceDirect Journals Complete 版權： 2013 Elsevier B.V. 版權： 2015 INIST-CNRS 識別號： ISSN: 0169-555X 識別號： EISSN: 1872-695X 識別號： DOI: 10.1016/j.geomorph.2013.11.020
