Modeling Typhoon Event-Induced Landslides Using GIS-Based Logistic Regression: A Case Study of Alishan Forestry Railway, Taiwan
Autor: | Long-Ming Huang, Sheng-Chuan Chen, Hsun-Chuan Chan, Li-Ling Lin, Chia-Chi Chang |
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Jazyk: | angličtina |
Rok vydání: | 2013 |
Předmět: |
geography
Variables geography.geographical_feature_category Article Subject Receiver operating characteristic General Mathematics media_common.quotation_subject Bedrock lcsh:Mathematics General Engineering Landslide Forestry Logistic regression lcsh:QA1-939 Hazard Ranking lcsh:TA1-2040 Typhoon lcsh:Engineering (General). Civil engineering (General) Geology media_common |
Zdroj: | Mathematical Problems in Engineering, Vol 2013 (2013) |
ISSN: | 1563-5147 |
Popis: | 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. |
Databáze: | OpenAIRE |
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