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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/36064

    Title: 以複合型模式分析區域淹水潛勢
    Other Titles: Regional potential inundation analysis using hybrid models
    Authors: 鄭伊婷;Cheng, I-ting
    Contributors: 淡江大學水資源及環境工程學系碩士班
    張麗秋;Chang, Li-chiu
    Keywords: 倒傳遞類神經網路、區域淹水;K-Means聚類分析;線性迴歸模式;淹水推估;back-propagation neural network;Regional Flood Inundation;K-means Clustering Analysis;Linear Regression Mode l;Flood Inundation Estimation
    Date: 2009
    Issue Date: 2010-01-11 07:32:03 (UTC+8)
    Abstract: 台灣在夏秋之際常受到颱風及豪雨侵襲,當颱風暴雨來臨時各地區無法及時排水造成中下游平原地區積淹水災情嚴重,需仰賴淹水潛勢圖做為淹水影響範圍及深度之評估資訊,達到事前防災的效果。傳統淹水潛勢圖模擬過程,需要大量的輸入資訊及經過繁複演算方可推估區域淹水潛勢,不僅造成電腦系統運算負擔亦無法達到防災工作所需的即時效果,且只能提供特定降雨量下之淹水情況查詢。本研究提出以複合型模式建立小區域即時淹水災害範圍推估,以獲得即時洪災資訊及災害影響範圍。
    The typhoon events usually cause downstream flooding in Taiwan. Estimation the flood depths and extent may provide the flood inundation information that will be helpful to deal with some contingencies, then alleviate the risk and losses of the flood disasters. The conventional simulations of flood inundation extent need a huge amount of data and computing time by using a series of numerical models. The study proposes the hybrid models to build the regional flood inundation estimation model. In order to figure out the causes of the flood inundation, the correlation analysis and factor analysis are used to explore the relationship between flood inundation influence factors; then K-means clustering is used to categorize the data points of the different flooding characteristics and to find the control point in each flooding group. The hybrid models are composed of three types of models in each flooding group: a single 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. Two study areas, Fonshang city, Kaohsiung County, and Yuanlin township, Changhua County, are tested for evaluating the practicability and accuracy of the proposed approach. The results show that the proposed hybrid models can accurately and timely estimate the simulated flood inundation extents and depths.
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Thesis

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