土石流潛感分析－以石門水庫集水區為例; Debris flow susceptibility analysis – A case study in Shihmen Reservoir Watershed

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/5686

Title:	土石流潛感分析－以石門水庫集水區為例;Debris flow susceptibility analysis – A case study in Shihmen Reservoir Watershed
Authors:	張舜琦;Shun-chi Chang
Contributors:	應用地質研究所
Keywords:	模糊類神經網路;羅吉斯迴歸;判別分析;潛感;土石流;fuzzy neural network;logistic regression;discriminant analysis;susceptibility;debris flow
Date:	2007-07-03
Issue Date:	2009-09-22 09:59:38 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	本研究針對大漢溪流域石門水庫上游集水區，以溪床坡度及地形隘口為主要依據定義土石流及非土石流溪流之溢流點，並以溢流點以上之集水區為單位計算集水面積、主流長度、形狀係數、集水區相對高度、溪床坡度、殘土率、發生區面積、崩塌地面積、事件總雨量等因子，代入判別分析、羅吉斯迴歸和模糊類神經網路輸出各土石流潛感值與分類結果。本研究主要分為歷年土石流潛感分析與艾利颱風誘發土石流潛感分析兩部份。研究中使用水保局公布之土石流潛勢溪流為歷年土石流圖層，使用判別分析、羅吉斯迴歸和模糊類神經網路進行歷年土石流潛感分析，得到之總體正確率分別為76.8%、78.6%和94.6%。其中模糊類神經網路在歷年土石流潛感分析中有最高之判釋正確率。艾利颱風誘發土石流潛感分析主要是依據事件前後衛星影像變異點決定土石流溪流，以溢流點以上集水區為單位進行分析。得到判別分析、羅吉斯迴歸和模糊類神經網路三種分析方法的總體正確率為94.1%、100.0%和98.5%的高值。總體而言羅吉斯迴歸得到最好的判釋結果。比較歷年土石流與艾利颱風誘發土石流潛感分析結果可以發現，艾利颱風誘發土石流潛感分析可以得到較好之正確率結果。事件誘發土石流潛感分析中除了對土石流及非土石流溪流的認定較為確實外，同時加入了雨量因子，因此可以得到較好的分析結果。在歷年土石流潛感分析中，土石流圖層多為包含保全對象的土石流潛勢溪流，然而本研究並未使用保全因子，因此得到較事件誘發土石流潛感分析略差的分析結果。 In this study, factors used in the susceptibility analysis includes the watershed area, the length of main stream, the shape factor, relative height of watershed, slope of main stream, hypsometric integral, area of watershed slope great than 20°, area of landslide, and total rainfall of a storm event. These factors were calculated for each unit of a sub-watershed which is defined by an overflow point. Then these factors were used as input into discriminant analysis, logistic regression and fuzzy neural network to evaluate sub-watershed’s debris flow susceptibility in Shihmen Reservoir Watershed. This study focus on two different kind of cases: historical debris flow susceptibility analysis and the AERE typhoon induced debris flow susceptibility analysis. In the historical debris flow susceptibility analysis, the potential debris flow streams were acquired from the Soil and Water Conservation Bureau, Taiwan (WCB). The overall accuracy of discriminant analysis, logistic regression, and fuzzy neural Network are 76.8%, 78.6%, and 94.6%, respectively. Fuzzy neural network has the highest overall accuracy in historical debris flow susceptibility analysis. The debris flows induced by AERE typhoon are base on the changes detected from satellite images before and that after the typhoon event. The overall accuracy of discriminant analysis, logistic regression and fuzzy neural network are 94.1%, 100.0%, and 98.5%, respectively. Logistic regression method has the best performance in AERE typhoon induced debris flow susceptibility analysis. From the comparison of historical debris flow susceptibility analysis and AERE typhoon induced debris flow susceptibility analysis, we can find that AERE typhoon induced debris flow susceptibility analysis has better accuracy. This is because the AERE typhoon induced debris flow susceptibility analysis has more definite debris flow data and also because the rainfall data as an import factor are considered. On the other hand and in the historical debris flow susceptibility analysis, most of debris flow streams are not well defined and mixed with the consideration of property lose, therefore , a less accuracy was presented in the result of analysis.
Appears in Collections:	[Graduate Institute of Applied Geology] Electronic Thesis & Dissertation

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