教師資料查詢 | 類別: 期刊論文 | 教師: 張麗秋 LI-CHIU CHANG (瀏覽個人網頁)

標題:Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling
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出版(發表)日期2004/05/01
作品名稱Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling
作品名稱(其他語言)
著者張麗秋; Chang, Li-chiu; Chiang, Yen-ming; Chang, Fi-john
單位淡江大學水資源及環境工程學系
出版者Elsevier B.V
著錄名稱、卷期、頁數Journal of Hydrology 290(3-4), pp.297-311
摘要A systematic comparison of two basic types of neural network, static and dynamic, is presented in this study. Two back-propagation (BP) learning optimization algorithms, the standard BP and conjugate gradient (CG) method, are used for the static network, and the real-time recurrent learning (RTRL) algorithm is used for the dynamic-feedback network. Twenty-three storm-events, about 1632 rainfall and runoff data sets, of the Lan-Yang River in Taiwan are used to demonstrate the efficiency and practicability of the neural networks for one hour ahead streamflow forecasting. In a comparison of searching algorithms for a static network, the results show that the CG method is superior to the standard BP method in terms of the efficiency and effectiveness of the constructed network's performance. For a comparison of the static neural network using the CG algorithm with the dynamic neural network using RTRL, the results show that (1) the static-feedforward neural network could produce satisfactory results only when there is a sufficient and adequate training data set, (2) the dynamic neural network generally could produce better and more stable flow forecasting than the static network, and (3) the RTRL algorithm helps to continually update the dynamic network for learning—this feature is especially important for the extraordinary time-varying characteristics of rainfall–runoff processes.
關鍵字Rainfall–runoff processes; Streamflow forecasting;Neural networks;Static systems;Dynamic systems
語言英文
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期刊性質國內
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產學合作
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審稿制度
國別中華民國
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