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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94584

    题名: 應用希爾伯特-黃轉換法與極速學習機於逕流量及颱風降雨之預測研究
    其它题名: Prediction of monthly discharge and typhoon rainfall based on Hilbert-Huang transform and ELM
    作者: 王僑宏;Wang, Chiao-Hung
    贡献者: 淡江大學水資源及環境工程學系碩士班
    黃富國;Huang, Fu-Kuo
    关键词: 逕流量;颱風降雨;經驗模態分解;極速學習機;ARIMA模式;Discharge;Typhoon Rainfall;Empirical mode decomposition;Extreme Learning Machine;ARIMA Model
    日期: 2013
    上传时间: 2014-01-23 14:47:39 (UTC+8)
    摘要: 台灣地區山高坡陡,河道源短流急,水資源蓄積不易﹔加上空間與時間上分布不均勻,以及全球氣候變遷影響,時有缺水情形發生。因此亟需一個準確性較佳之預測模式來有效調配水資源。本研究以(季節性)自回歸積分移動平均模式(ARIMA、SARIMA)、希爾伯特-黃轉換(HHT)中之經驗模態分解(EMD)方法,以及類神經網路中的極速學習機(ELM)等各模式組合而成多種複合預測模式,並採用大漢溪流域石門水庫上游之月流量,以及荖濃溪流域在莫拉克(Morakot)颱風與賀伯(Herb)颱風之降雨為例,分別進行長時間尺度之逕流量,與短時間尺度之颱風時雨量之預測準確性的探討及分析,作為水資源預測模式採擇之參考。
    Water is essential to life, but the water resource of Taiwan is limited and hard to retain for most of the rivers run from high mountains in short and steep courses. In addition, the temporal and spatial distribution of rainfall is very uneven. How to allocate the water resources rationally becomes an important issue and a better prediction method with a higher accuracy is necessary in response to global climate change.
    In this study, several hybrid prediction models are employed based on autoregressive-integrated-moving average (ARIMA、SARIMA), empirical mode decomposition(EMD) of Hilbert-Huang transform (HHT), and extreme learning machine(ELM). Two case studies are presented according to the data of the upstream monthly riverflow of Shihmen Reservoir in Tahan River Basin and the rainfall of Laonong River Basin during typhoon Morakot and Herb. It is shown that for long-time scale discharge, ELM model has the better performance of prediction. However, HHT_ELM is superior to ELM at the section of the larger data values. On the other hand, for short-time scale rainfall of typhoon, ELM and HHT_extension models behave well in view of sequence characteristics of rainfall.
    显示于类别:[水資源及環境工程學系暨研究所] 學位論文


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