Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example
碩士 === 國立中興大學 === 水土保持學系所 === 102 === As pointed out in many previous studies, climate change due global warming will result in the increases of the frequencies and intensities of storm events;Due to fragile geology, soil and torrential rain leads to severe erosion. Furthermore, increasing populatio...
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ndltd-TW-102NCHU50800812017-01-26T04:21:02Z http://ndltd.ncl.edu.tw/handle/15064806068838018655 Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example 以羅吉斯迴歸法建立陳有蘭溪集水區山崩潛感圖之研究 Yue-Sheng Ou 區悅生 碩士 國立中興大學 水土保持學系所 102 As pointed out in many previous studies, climate change due global warming will result in the increases of the frequencies and intensities of storm events;Due to fragile geology, soil and torrential rain leads to severe erosion. Furthermore, increasing population and overdevelopment brings even greater damage to the land. The site of this study was selected at Chenyulan stream watershed.The study focuses on the landslides induced by the Typhoon Sinlaku occurring in 2008 and the Typhoon Morakot occurring in 2009.This study used GIS as a tool to map storm-induced landslides from SPOT images. Digital elevation model (DEM) was used to extract geomorphic landslide causative factors. SPOT image was also used to calculate an environmental factor - NDVI (normalized differential vegetation index). This study analyzes 8 factors including elevation, slope, aspect, relief, roughness, distance to roads and distance to rivers. Using the hourly maximum rainfall related to spatial information by typhoon event as trigger factor. We sample equal cell number of data randomly for landslide group and non-landslide group, then input those data to SPSS statistical software and build a logistic model for the study area. Furthermore, error matrix was classified using of classification accuracy to evaluate the effects of causative factors on the landslides at watershed. The result shows that the overall accuracy in typhoon Sinlaku and typhoon Morakot these two events are 92.2% and 90.2% respectively. Most of the actual landslide data fall in the high-moderate and high susceptibility class respectively. It indicates that the results are satisfactory. The landslide potential maps in this research can provide to supervisor of watershed to monitor the landslide. 陳文福 張楨驩 2014 學位論文 ; thesis 82 zh-TW |
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碩士 === 國立中興大學 === 水土保持學系所 === 102 === As pointed out in many previous studies, climate change due global warming will result in the increases of the frequencies and intensities of storm events;Due to fragile geology, soil and torrential rain leads to
severe erosion. Furthermore, increasing population and overdevelopment brings even greater damage to the land.
The site of this study was selected at Chenyulan stream
watershed.The study focuses on the landslides induced by the Typhoon Sinlaku occurring in 2008 and the Typhoon Morakot occurring in 2009.This study used GIS as a tool to map storm-induced landslides from SPOT images. Digital elevation model (DEM) was used to extract geomorphic landslide causative factors. SPOT image was also used to
calculate an environmental factor - NDVI (normalized differential vegetation index). This study analyzes 8 factors including elevation, slope, aspect, relief, roughness, distance to roads and distance to rivers. Using the hourly maximum rainfall related to spatial information by typhoon event as trigger factor.
We sample equal cell number of data randomly for landslide group and non-landslide group, then input those data to SPSS statistical software and build a logistic model for the study area. Furthermore, error matrix was classified using of classification accuracy to evaluate the effects of causative factors on the landslides at
watershed. The result shows that the overall accuracy in typhoon Sinlaku and typhoon Morakot these two events are 92.2% and 90.2% respectively. Most of the actual landslide data fall in the high-moderate and high susceptibility class respectively. It indicates that the results are satisfactory. The landslide potential maps in this research can provide to supervisor of watershed to monitor the landslide.
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author2 |
陳文福 |
author_facet |
陳文福 Yue-Sheng Ou 區悅生 |
author |
Yue-Sheng Ou 區悅生 |
spellingShingle |
Yue-Sheng Ou 區悅生 Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example |
author_sort |
Yue-Sheng Ou |
title |
Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example |
title_short |
Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example |
title_full |
Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example |
title_fullStr |
Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example |
title_full_unstemmed |
Application of the Logistic Regression Method in Landslide Susceptibility Mapping-Using Chenyulan Stream Watershed as an example |
title_sort |
application of the logistic regression method in landslide susceptibility mapping-using chenyulan stream watershed as an example |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/15064806068838018655 |
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