A two-step-ahead recurrent neural network for stream-flow forecasting
學年 92
學期 1
出版（發表）日期 2004-01-01
作品名稱 A two-step-ahead recurrent neural network for stream-flow forecasting
作品名稱（其他語言）
著者 張麗秋; Chang, Li-chiu; Chang, F. J.; Chiang; Y. M
單位 淡江大學水資源及環境工程學系
出版者 Wiley Online
著錄名稱、卷期、頁數 Hydrological processes 18(1), pp.81-92
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.
關鍵字
語言 en
ISSN
期刊性質 國內
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產學合作
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審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版
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