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  • 學位論文

結合聚類法與類神經網路發展颱風淹水預警系統

Development of A Typhoon Inundation Warning System by Integrating Clustering and Neural Networks

指導教授 : 林國峰
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摘要


颱風或豪雨襲臺期間,時常會造成淹水的災害。因此,淹水預警系統現在對於防災單位相當重要,如何建立準確的預報模式,以至於進一步得知區域預報的淹水深度結果,是本研究的重點。 本文結合k-means聚類法和新型類神經網路-支援向量機(support vector machine, SVM)發展一套區域淹水預警系統,主要架構分成三部分:分類、預報以及空間推估。首先,將所收集淹水區資料,使用k-means聚類法根據淹水歷線進行分類,所分出的每個類別即為一種淹水型態,每個類別中可對應出各控制點位置。進而,在各個控制點建構預報模式,利用降雨量和淹水深度兩個因子作為SVM預報模式的輸入項,預報控制點未來1至3小時的淹水深度。接著將各控制點預報的淹水深度,以及與控制點分至相同類別網格點的二度分帶座標(X、Y)、高程和雨量這5個因子,利用SVM空間推估模式,即可推估未來1至3小時,所有網格點的淹水深度。 本研究以雲林縣的西螺鎮來驗證所提出的方法。結果顯示,利用區域淹水預警系統能夠準確的預報未來1至3小時的淹水深度。最後,網格點的預報淹水深度利用地理資訊系統(geographic information system, GIS)繪製,預報結果的淹水深度圖能夠反應出所收集到的淹水潛勢圖資料。

並列摘要


During typhoons, flood inundation caused by rainfall often leads to loss of life and property damage. For inundation mitigation, development of a regional inundation warning system has been recognized as an important task in hydrology. In this study, an accurate and effective regional inundation warning system by integrating k-means clustering and support vector machine (SVM) is proposed. The proposed regional inundation warning system consists of three parts: classification, forecasting and extension. Firstly, the inundation depth hydrographs are clustered by k-means clustering, which is a useful technique for solving classification problems. The inundation depth hydrographs with specific different characteristics are classified and the center of each cluster is seen as a control point in this study. Secondly, the rainfall and inundation depth are used as inputs to develop the SVM-based inundation forecasting model for each control point. Thirdly, the point forecasts resulting from the SVM-based inundation forecasting model are extended to the spatial forecasts by using the SVM-based extension model. The input variables of the SVM-based extension model are the coordinates, the elevation and the rainfall of forecasted point and the forecasting result of the control point. An actual application of the proposed regional inundation warning system in the Xiluo Township is conducted to demonstrate the advantages of the proposed system. The results show that the proposed regional inundation warning system can effectively forecasting the inundation depth, and the proposed regional inundation warning system is expected to be useful to mitigate the inundation damage.

參考文獻


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被引用紀錄


陳子裕(2017)。結合K-means法與類神經網路建立用電量推估抽水量模式-以濁水溪沖積扇為例〔碩士論文,國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342%2fNTU201703923

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