A two-step-ahead recurrent neural network for stream-flow forecasting

doi:10.1002/hyp.1313

淡江大學機構典藏 > 工學院 > 水資源及環境工程學系暨研究所 > 期刊論文 > Item 987654321/67794

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题名:	A two-step-ahead recurrent neural network for stream-flow forecasting
作者:	張麗秋;Chang, Li-chiu;Chang, F. J.;Chiang;Y. M
贡献者:	淡江大學水資源及環境工程學系
日期:	2004-01-01
上传时间:	2011-10-23 02:03:58 (UTC+8)
出版者:	Wiley Online
摘要:	In many engineering problems, such as flood warning systems, accurate multistep-ahead prediction is critically important. The main purpose of this study was to derive an algorithm for two-step-ahead forecasting based on a real-time recurrent learning (RTRL) neural network that has been demonstrated as best suited for real-time application in various problems. To evaluate the properties of the developed two-step-ahead RTRL algorithm, we first compared its predictive ability with least-square estimated autoregressive moving average with exogenous inputs (ARMAX) models on several synthetic time-series. Our results demonstrate that the developed two-step-ahead RTRL network has efficient ability to learn and has comparable accuracy for time-series prediction as the refitted ARMAX models. We then investigated the two-step-ahead RTRL network by using the rainfall–runoff data of the Da-Chia River in Taiwan. The results show that the developed algorithm can be successfully applied with high accuracy for two-step-ahead real-time stream-flow forecasting. Copyright © 2003 John Wiley & Sons, Ltd.
關聯:	Hydrological processes 18(1), pp.81-92
DOI:	10.1002/hyp.1313
显示于类别:	[水資源及環境工程學系暨研究所] 期刊論文

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