Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan
碩士 === 國立臺灣大學 === 土木工程學研究所 === 103 === On average, three to four typhoons attack Taiwan each year. Although typhoon rainfall is an important source of water resources, the heavy rainfall brought by typhoons frequently result in serious disasters. Landslide is one of the most destructive slope disast...
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ndltd-TW-103NTU050151922019-05-15T22:17:25Z http://ndltd.ncl.edu.tw/handle/s376nh Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan 崩塌潛勢分析方法之研究-以高屏溪流域為例 Ya-Chiao Huang 黃雅喬 碩士 國立臺灣大學 土木工程學研究所 103 On average, three to four typhoons attack Taiwan each year. Although typhoon rainfall is an important source of water resources, the heavy rainfall brought by typhoons frequently result in serious disasters. Landslide is one of the most destructive slope disasters. Therefore, to establish a landslide susceptibility model, which can efficiently mitigate the disaster, is always an important task of slope disaster management. In this study, three methods are employed to construct landslide susceptibility models for the Kaoping River basin in southern Taiwan, and then the model performances of these three models are compared. The three methods include the conventional logistic regression (LR) and two novel machine learning methods, namely, Support Vector Machine (SVM) and Improved Self-organizing Linear Output Map (ISOLO). Landslide events from 2008 to 2011 are collected. The first three-year data from 2008 to 2010 are used in the training phase of the models, and the remaining data are for testing. Moreover, fourteen landslide-related factors are used in the landslide susceptibility analysis, such as slope, slope aspect, elevation, curvature, profile curvature, plan curvature, slope length, topographic wetness index, distance to river, distance to road, distance to fault, 24-hour rainfall and 48-hour rainfall. The performances of three models are checked by the accuracy and the area under the receiver operating characteristic curve (AUC). The results show that the ISOLO model outperforms over the LR and SVM models in the study area. Landslide susceptibility maps obtained from the proposed model are expected to be helpful to local administrations and decision makers in disaster planning. Gwo-Fong Lin 林國峰 2015 學位論文 ; thesis 87 zh-TW |
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碩士 === 國立臺灣大學 === 土木工程學研究所 === 103 === On average, three to four typhoons attack Taiwan each year. Although typhoon rainfall is an important source of water resources, the heavy rainfall brought by typhoons frequently result in serious disasters. Landslide is one of the most destructive slope disasters. Therefore, to establish a landslide susceptibility model, which can efficiently mitigate the disaster, is always an important task of slope disaster management.
In this study, three methods are employed to construct landslide susceptibility models for the Kaoping River basin in southern Taiwan, and then the model performances of these three models are compared. The three methods include the conventional logistic regression (LR) and two novel machine learning methods, namely, Support Vector Machine (SVM) and Improved Self-organizing Linear Output Map (ISOLO). Landslide events from 2008 to 2011 are collected. The first three-year data from 2008 to 2010 are used in the training phase of the models, and the remaining data are for testing. Moreover, fourteen landslide-related factors are used in the landslide susceptibility analysis, such as slope, slope aspect, elevation, curvature, profile curvature, plan curvature, slope length, topographic wetness index, distance to river, distance to road, distance to fault, 24-hour rainfall and 48-hour rainfall.
The performances of three models are checked by the accuracy and the area under the receiver operating characteristic curve (AUC). The results show that the ISOLO model outperforms over the LR and SVM models in the study area. Landslide susceptibility maps obtained from the proposed model are expected to be helpful to local administrations and decision makers in disaster planning.
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Gwo-Fong Lin |
author_facet |
Gwo-Fong Lin Ya-Chiao Huang 黃雅喬 |
author |
Ya-Chiao Huang 黃雅喬 |
spellingShingle |
Ya-Chiao Huang 黃雅喬 Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan |
author_sort |
Ya-Chiao Huang |
title |
Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan |
title_short |
Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan |
title_full |
Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan |
title_fullStr |
Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan |
title_full_unstemmed |
Landslide susceptibility mapping methodologies for the Kaoping River basin, Taiwan |
title_sort |
landslide susceptibility mapping methodologies for the kaoping river basin, taiwan |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/s376nh |
work_keys_str_mv |
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