Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling
學年 92
學期 2
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 en
ISSN
期刊性質 國內
收錄於
產學合作
通訊作者
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版
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