English  |  正體中文  |  简体中文  |  Items with full text/Total items : 56552/90363 (63%)
Visitors : 11832146      Online Users : 112
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/67794

    Title: A two-step-ahead recurrent neural network for stream-flow forecasting
    Authors: 張麗秋;Chang, Li-chiu;Chang, F. J.;Chiang;Y. M
    Contributors: 淡江大學水資源及環境工程學系
    Date: 2004-01-01
    Issue Date: 2011-10-23 02:03:58 (UTC+8)
    Publisher: Wiley Online
    Abstract: 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.
    Relation: Hydrological processes 18(1), pp.81-92
    DOI: 10.1002/hyp.1313
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

    Files in This Item:

    There are no files associated with this item.

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback