Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 728304, 9 pages
Research Article

Modeling Typhoon Event-Induced Landslides Using GIS-Based Logistic Regression: A Case Study of Alishan Forestry Railway, Taiwan

Department of Soil and Water Conservation, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 402, Taiwan

Received 30 April 2013; Revised 22 July 2013; Accepted 23 July 2013

Academic Editor: Gerhard-Wilhelm Weber

Copyright © 2013 Sheng-Chuan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study develops a model for evaluating the hazard level of landslides at Alishan Forestry Railway, Taiwan, by using logistic regression with the assistance of a geographical information system (GIS). A typhoon event-induced landslide inventory, independent variables, and a triggering factor were used to build the model. The environmental factors such as bedrock lithology from the geology database; topographic aspect, terrain roughness, profile curvature, and distance to river, from the topographic database; and the vegetation index value from SPOT 4 satellite images were used as variables that influence landslide occurrence. The area under curve (AUC) of a receiver operator characteristic (ROC) curve was used to validate the model. Effects of parameters on landslide occurrence were assessed from the corresponding coefficient that appears in the logistic regression function. Thereafter, the model was applied to predict the probability of landslides for rainfall data of different return periods. Using a predicted map of probability, the study area was classified into four ranks of landslide susceptibility: low, medium, high, and very high. As a result, most high susceptibility areas are located on the western portion of the study area. Several train stations and railways are located on sites with a high susceptibility ranking.