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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/88176

    Title: 結合經驗模態分解與類神經網路於地下水位預測之研究
    Other Titles: Prediction of groundwater level based on EMD and ANN
    Authors: 鄭鈞瑋;Cheng, Chun-Wei
    Contributors: 淡江大學水資源及環境工程學系碩士班
    黃富國;Huang, Fu-Kuo
    Keywords: 地下水位;經驗模態分解;類神經網路;自組特徵映射網路;預報模式;groundwater level;empirical mode decomposition(EMD);artificial nearul network(ANN);self-organizing map(SOM);real forecast
    Date: 2012
    Issue Date: 2013-04-13 12:04:19 (UTC+8)
    Abstract: 台灣地區山高坡陡,河道源短流急,水資源蓄積不易,且近年全球環境變化甚鉅,時有缺水情形發生。台灣地區地下水用量約佔整體水資源可利用量之三成,地下水之重要性不言可喻。因此有必要深入探討地下水位變化之規律,以掌握其脈動及特性。為達成此目標,則有賴於更具真實性與準確性之地下水位預測模式,所以本研究在既有常用預測方法之基礎上,進一步尋求一更合理可行之預測模式。
    Recently, the change of hydrological environment is being accelerated significantly by the impact of global warming due to climate change. It is important for the management of groundwater resources because of limited water resource. However, the use of groundwater resources efficiently relies on a more real and precise prediction model. In this study, it will seek for a reasonable and effective way to predict the changes of groundwater level by modifying the traditional method.
    HHT is a relatively new method to analyze time series data that possess intrinsic non-linear and non-stationary nature. By using EMD of HHT, complicated data will decompose into several IMFs that are easier to analyze. The “real-forecast” model combined from EMD, SOM and BPNN will further applied to predict the groundwater level. It shows that the predict result is more accurate than that from the traditional method. Among them, the mode of single station and multi station is adopted, but the combination mode with best performance needs to be appropriately evaluated in accordance with the characteristics of these stations.
    Appears in Collections:[水資源及環境工程學系暨研究所] 學位論文

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