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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/54723


    Title: 區域型內水淹水警示技術研發應用-以宜蘭縣為例
    Authors: 張麗秋
    Contributors: 淡江大學水資源及環境工程系
    Keywords: 類神經網路;聚類分析;區域淹水預測模式;Artificial neural networks;Clustering analysis;Regional flood inundation estimation
    Date: 2010
    Issue Date: 2011-07-06 22:32:18 (UTC+8)
    Abstract: 宜蘭地區近幾年飽受颱風重創之苦,尤其是97年辛樂克、薔蜜颱風與98年芭瑪颱風,適逢秋颱與東北季風形成共伴效應,連日豪雨不斷,多處災情頻傳。強化防災體系之應變措施與建置淹水預警機制,有助於水患災情防救之緊急應變,期以達到減少洪災損失之效。 淹水潛勢圖為二維水理模式之模擬結果,在高精度地形數值的需求下,數值模式需配置功能強大的計算資源,即時數值模擬需耗費較長的演算時間,對於洪災防救的緊急應變時間不足,使得二維水理模擬模式無法提供即時淹水預警,僅作為河川防洪、改善地區排水工程、訂定都市計畫及防災計畫與應變措施之參考。為延伸淹水潛勢圖之應用範圍,並提升淹水潛勢資料在災害防救計畫之參考價值,本計畫擬以淹水潛勢資料建置區域型內水淹水深預測模式,可提供颱風暴雨時期進行線上即時預測區域淹水情況,有助於防災單位掌握區域之淹水程度及範圍。 本計畫擬以聚類分析為基礎之複合型淹水模式建置宜蘭縣區域型內水淹水預警與推估模式。模式建構過程中可依序分為資料前處理階段與模式建置階段,前者以K-means聚類分析劃分不同淹水特性之區域範圍,求得各分群之控制點作為淹水特性之代表;後者則分別建置控制點之淹水預測模式、線性網格點之線性迴歸淹水預測模式、非線性網格點之倒傳遞類神經網路多點淹水預測模式。模式除採用設計雨型作為訓練資料外,也採用實際颱風豪雨事件之淹水模擬資料作為測試之用,以評估其整體預測成效;並提供自動化即時線上預報程式,具體地將本計畫所建置之模式上線使用。最後,將研究成果彙整投稿於國際期刊。
    Yilan frequently suffers from severe flood disasters in recent years. Especially, the interplay of typhoons and northeast monsoon results in heavy rainfall and serious flood disasters, such as the 2008 typhoon Sinlaku , Jangmi and the 2009 typhoon Parma. Therefore, strengthening the disaster defense systems and establishing the flood inundation forecasting systems will be helpful to deal with some contingencies and emergencies, then alleviate the risk and losses of the disasters. The flood inundation potential maps were generated by a series of numerical simulation models that would take a huge amount of time to generate the flood inundation potential database with high resolution DEMs, but the maps are not to provide the forecasting information of flood inundation extent. Hence, that would not provide enough time for the flood defense strategies, only for the planning of drainage improvement engineering, flood control, and Metropolis plan. In order to enhance the usages and applications of the flood inundation potential maps, this project will establish the regional flood inundation forecasting model based on the flood inundation potential database and expect the forecasting model can provide the forecasting information to the disaster defense departments. In this project, we propose a clustering-based hybrid inundation model to construct the regional flood inundation forecasting model for the Yilan county. The procedure of this approach is divided into two stages: data preprocessing and model building. In the data preprocessing stage, K-means clustering is used to categorize the flooding area of the different flooding characteristics in the study area and to determine a control point from individual flooding cluster(s). In the model building stage, three types of models were built in each group: a single-grid back-propagation neural network (BPNN) for each control point, the linear regression models for the linear grids and a multi-grid BPNN for the nonlinear grids. This project will use the simulation data of the designed events as training data and the simulation data of typhoon events as testing data to evaluate the practicability and effectiveness of the proposed approach. Moreover, this project will provide an on-line forecasting system to implement the proposed regional flood inundation model. Finally, we would submit this methodology to the international journals.
    Appears in Collections:[水資源及環境工程學系暨研究所] 研究報告

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