Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling

doi:10.1016/j.jhydrol.2003.12.033

機構典藏 > College of Engineering > Graduate Institute & Department of Water Resources and Environmental Engineering > Journal Article > Item 987654321/67818

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/67818

Title:	Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling
Authors:	張麗秋;Chang, Li-chiu;Chiang, Yen-ming;Chang, Fi-john
Contributors:	淡江大學水資源及環境工程學系
Keywords:	Rainfall–runoff processes;Streamflow forecasting;Neural networks;Static systems;Dynamic systems
Date:	2004-05-01
Issue Date:	2011-10-23 02:05:31 (UTC+8)
Publisher:	Elsevier B.V
Abstract:	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.
Relation:	Journal of Hydrology 290(3-4), pp.297-311
DOI:	10.1016/j.jhydrol.2003.12.033
Appears in Collections:	[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

Files in This Item:

File	Description	Size	Format
Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling.pdf		755Kb	Adobe PDF	1	View/Open
index.html		0Kb	HTML	22	View/Open

Loading...