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  • 學位論文

土石流危險溪流之發生頻度評估

Assessment on the Occurrence Frequency of Debris Flow Hazardous Gullies

指導教授 : 曾志民

摘要


本文主要目的為建置土石流危險溪流之發生頻度評估模式,吾人定義此一發生頻度為土石流危險溪流於一段時間內之災害發生平均次數,藉以表示此危險溪流可能發生土石流災害之潛在程度。研究方式首先選定影響土石流發生之相關因子,依其地文及水文特性針對危險溪流進行分群,並且經由土石流災害記錄以及Wilk’s λ值驗証群體差異度,其次透過模糊分群之隸屬度概念,將群體之土石流平均發生頻度分配至單一危險溪流。最後利用類神經網路建置土石流發生因子與發生頻度之關係,並且透過網路評鑑指標建立最合適之模式架構,以供後續若有新增危險溪流,便可快速評估土石流危險溪流之發生頻度。 本文以中部地區413條危險溪流為例,選定50年重現期60分鐘降雨延時之平均降雨強度、集水區面積、集水區平均坡度、溪流平均坡度、集水區形狀係數、崩塌比、岩性變質程度、道路開發程度等八個因子。本研究採用模糊分群方式,可將中部地區413條危險溪流分成高、中、低三種不同群體,其土石流平均發生頻度值分別為0.97、0.71及0.56。其次利用危險溪流對於各群體之隸屬度,進一步推求估算各危險溪流之土石流發生頻度,分析成果顯示中部地區413條土石流危險溪流發生頻度主要介於0.56∼0.97之間。透過實際災害紀錄檢視,土石流災害發生次數在3次以上者,頻度值大致在0.70以上,而頻度值在0.60以下者,大致無土石流災害發生的記錄或者只有一次土石流記錄者為多,評估結果尚稱良好。 最後本文選取中部地區413條危險溪流中之331條危險溪流作為類神經網路訓練範例,82條危險溪流作為測試範例,並以最陡坡降法及Levenberg-Marquardt(LM)法進行網路架構調整與參數設定。經由評鑑指標綜合分析後顯示,當網路架構採用2層隱藏層且各層具有3個神經元時,利用LM法之倒傳遞類神經網路有較佳的網路效能,其平均絕對誤差百分比(MAPE)值為2.11 %,且相關係數(r2)為0.97。由此顯示,本研究建立之土石流危險溪流發生頻度評估模式可提供相當程度之可信賴度。

並列摘要


Owing to the steep morphology, young and weak geological environment, heavy rainfall and inappropriate land use, Taiwan is subject to occurrence of debris flow, which often causes enormous human and material losses. Hence, the forecasting of debris flow is important to provide advance warning of debris flow. Due to lacking of enough disaster occurrence records to setup the warning criteria for each debris flow gully, the objective of the study is to develop the evaluation model on assessment of occurrence frequency of debris flow hazardous gullies. In the present study, the eight factors related to the occurrence of debris flow including rainfall intensity, watershed area, average slope of watershed and gullies, shape factor of watershed, landslide ratio, geological patterns and land-use were adopted to clustering 413 debris flow hazardous gullies in the Central Taiwan. The variance between different clustering groups was verified through Wilk’s value and disaster occurrence records. Clustering results show that 413 debris flow hazardous gullies in the central Taiwan can be reasonably divided into 3 groups, and the average occurrence frequency in three main groups is 0.97, 0.71 and 0.56, respectively. The occurrence frequency was then estimated according to the membership function of each gully belong to three main groups. The numbers of debris flow occurrence usually greater than 3 when frequency exceed 0.7. The numbers of debris flow occurrence always less than 2 when frequency under 0.6. Hence, artificial neural networks method was applied to develop the evaluation model on assessment of occurrence frequency of debris flow hazardous gullies. 331 debris flow hazardous gullies were used to training sample and 82 gullies were used to test sample in neural network. Based on the different algorithms, two models were tested and compared. The result shows that the Liebenberg-Marquardt (LM) method with two hidden layers, and three neurons in each hidden layer, has better performance in occurrence frequency forecasting. The value of mean absolute percentage error (MAPE) and correlation coefficient (r2) is 2.11% and 0.97, respectively.

參考文獻


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被引用紀錄


李宗賢(2011)。土地利用對洪氾淹水影響之數值模擬〔碩士論文,長榮大學〕。華藝線上圖書館。https://doi.org/10.6833%2fCJCU.2011.00197
陳曉貞(2007)。土石流發生頻度之研究-以陳有蘭溪流域為例〔碩士論文,長榮大學〕。華藝線上圖書館。https://doi.org/10.6833%2fCJCU.2007.00133

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