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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/5581

    Title: 區域性山崩潛感分析方法探討-以石門水庫集水區為例;Probe into the Regional Landslide Susceptibility Analysis-a case study in the Shimen Reservoir Catchment Area
    Authors: 鐘意晴;Yi-Ching Chung
    Contributors: 地球物理研究所
    Keywords: 山崩;潛感分析;羅吉斯迴歸;預測率曲線;susceptibility analysis;logistic regression;prediction rate curve;Landslide
    Date: 2009-07-07
    Issue Date: 2009-09-22 09:57:09 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 本研究使用地調所集水區地質調查及山崩土石流調查與發生潛勢評估計畫在石門水庫上游集水區的基本資料,改進計畫中之潛感模式,整理艾利事件不同時間的雨量資料,利用不同雨量因子的選取,評估分析模型可能改善之空間。選用之潛在因子包括:岩性、坡度、艾利事件NDVI前期、坡度粗糙度、切向曲率、全坡高、相對坡高、溼度指數、斷層距。促崩因子選用艾利事件最大時雨量與艾利事件總雨量。山崩目錄由艾利事件前後及馬莎事件前後之SPOT衛星影像判釋崩塌地位置,並輔以像片基本圖、地形圖及野外查核作檢核, 建立艾利事件誘發山崩目錄來進行分析、馬莎事件誘發山崩目錄則用於模型驗證。本研究以羅吉斯迴歸為主要分析方法建立潛感模型並藉由不同額外組資料的加入擴大促崩因子的值域並希望能提高促崩因子之權重,使模型的預測效果更佳。 除了使用羅吉斯迴歸外,研究中亦利用判別分析及類神經網路方法對同一筆資料進行分析。分析結果顯示,三種方法之成功率曲線的AUC值分別為0.8579、0.8257及0.8771,皆有相當不錯之結果。利用馬莎颱風事件誘發山崩資料驗證結果,三種方法得到之AUC值分別為0.7867、0.7264及0.7726,驗證結果亦令人滿意。雖然類神經網路方法在建模型時獲得最高之AUC,但在驗證時卻不是最高;判別分析方法在建模型時AUC最低,驗證時也是最低。羅吉斯迴歸方法在建模型時不是最好,但驗證時卻為最高,故羅吉斯迴歸方法在建立區域性山崩預測模型時最為穩定。 This study uses data set from the “Geological Investigation, Landslide-Debris Flow Investigation and their Susceptibility Evaluation on Watershed” project of the Central Geological Survey, Taiwan (CGS), and aims to improve the susceptibility model developed in the project, by using different rainfall factors and sampling schemes. All the causative factors used in the project are selected in this study; including lithology, NDVI before Aere event, slope roughness, tangential curvature, total slope height, relative slope height, wetness index and fault distance. The final selection of trigger factors is maximum rainfall intensity and total rainfall of the Aere event. Landslide inventories interpreted from SOPT images before and after Aere typhoon event and those of the Matsa typhoon event, were checked by examining rectified aerial photographs, topographic maps, and in the field, so as to establish the event-based landslide inventories. The Aere inventory is used for establishment of susceptibility model and the Matsa inventory for validation. We use different sampling schemes in the study and choose logistic regression as the main analytical method to establish the susceptibility model. We wish to select a set of sample that can expand the range of rainfall values and also raise the weights of the trigger factors. An extra data set from landslide group is selected and is put into the non-landslide group try setting the rainfall value to critical rainfall. Besides the use of logistic regression for establish susceptibility model, we also adopt discriminant analysis and fuzzy neural network in the study. The results show that AUCs of the success rate curves for logistic regression, discriminant analysis, and fuzzy neural network are 0.8579, 0.8257 and 0.8771, respectively; all show good performance. The results are validated by the data set from of the Matsa typhoon event. The results of validation show that AUCs of the prediction rate curves are 0.7867, 0.7264 and 0.7726, respectively; it is also satisfactory. Although the fuzzy neural network has the highest AUC in establishing model, it is not the highest in validation; this phenomenon may be a kind of over-training. The discriminant analysis obtained the lowest AUC in establishing model and validation. The result of logistic regression is not the best one in establishing the model, but it is the best in validation. Therefore, the logistic regression is the most effective and stable method in establishing the regional landslide prediction model.
    Appears in Collections:[地球物理研究所] 博碩士論文

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