Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/89381
標題: Comparisons of Logistic regression, Instability index method and Support vector machine for landslide susceptibility mapping in the Jing-Shan River upstream Watershed
以羅吉斯回歸、不安定指數及支撐向量機建立景山溪上游集水區山崩潛感推估之比較分析
作者: 温祐霆
Yu-Ting Wen
關鍵字: 羅吉斯回歸
不安定指數
支撐向量機
山崩潛感
Logistic regression
Instability Index
Support vector machine
landslide susceptibility
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摘要: 景山溪為大安溪集水區之支流,其下游處為鯉魚潭水庫,係台灣一座離槽水庫,除了具有觀光、灌溉、防洪及發電四大功能外,主要提供苗栗縣及大台中地區民生用水。近年來,由於颱風豪雨事件之影響,造成景山溪上游集水區發生山崩,使泥砂順水流流入庫區,導致水庫壽命減少,對人民用水受到威脅,本研究以景山溪上游集水區為研究對象,預測出高山崩潛感區域加強防範,提供相關單位防護目標,期待降低泥砂帶入庫區增加水庫壽命。 故用以研究分析之方法,利用羅吉斯回歸、不安定指數及支撐向量機等三種模式,搭配經濟部中央地質調查所製作的山崩目錄,先初選10個潛在因子,再經複選過程選取6個解釋山崩能力較佳的因子,建立模式並繪製山崩潛感圖,分析結果以ROC曲線(receiver operating characteristic curve)評估三種不同模式預估景山溪上游集水區潛感之準確程度。 研究結果顯示,地形粗糙度因子於羅吉斯回歸及不安定指數分析方法中,對山崩潛感值有一定的影響程度;而不安定指數不僅在河道附近之潛感會有低估之情形,針對影響山崩潛感權重較大之因子,其分級之級數多寡亦會左右潛感分析結果;支撐向量機利用提供的資料在進行分類,其山崩潛感不會發生傾向於某個因子的情況,因此不會有因子權重擴大或縮小而影響模式判別的情況產生。另外,本研究將潛感圖分成低潛感、中潛感、中高潛感及高潛感,實際山崩地多座落於中潛感以上之區域,由ROC曲線下面積得知(Area under the curve, AUC),支撐向量機較其他兩種模式有較高的精確度,其AUC值0.825優於羅吉斯回歸AUC值0.721及不安定指數AUC值0.718,在研究區域內支撐向量機之山崩潛感分析結果較其他兩種模式優越。
The Jing-Shan River is a tributary of Da-An River watershed, which is located at Li-Yu-Tan reservoir in the downstream. The facility functions as not only an electricity generator, flood preventer, farmlands irrigator as well as a tourist attraction but also the water supplier of Miao-Li and Tai-Chung district. Recently, the torrential rain that come with typhoons and extreme weathers has caused many landslides in the watershed and severely shrunk the capacity and usability of reservoir. This study used the inventories of landslide established by Central Geological Survey as the landslide data. Logistic regression, Instability index method and Support vector machine (SVM) were selected to establish the landslide susceptibility models and obtain the landslide susceptibility maps in the upstream areas of Jing-Shan River. Ten landslide causative factors were first chosen as the landslide causative factors, according to the previous studies. A selection procedure was then performed to efficiently reduce the number of factors. Further, the receiver operating characteristic curve was used to evaluate the accuracy of model results. As a result, Logistic regression and Instability index method both show that the terrain roughness is a critical factor on the susceptibility value. The instability index method is not only lead to possible underestimation around the river side but also the number of factor classification can impact the result. SVM establish the model by classifying the landslide data. The landslide susceptibility values is not prone to particular factors. Therefore, the results of model prediction is not influenced by the weights of factor. Furthermore, the landslide susceptibilities is classified into four ranks, including: low, medium, medium-high, and high. SVM and logistic regression is suggested to be superior to Instability index method due to recognize the landslides located at the medium-high susceptibility areas. The analysis of area under the curve (AUC) showed AUC of 0.825 for SVM, 0.721 for the logistic regression, and 0.718 for the instability method. This further suggests SVM is a preferred method among the others in assessment of landslide in the research areas.
URI: http://hdl.handle.net/11455/89381
其他識別: U0005-1708201519331300
文章公開時間: 2018-08-19
Appears in Collections:水土保持學系

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