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https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/67794
題名:
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
貢獻者:
淡江大學水資源及環境工程學系
日期:
2004-01-01
上傳時間:
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
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[水資源及環境工程學系暨研究所] 期刊論文
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