淡江大學機構典藏:Item 987654321/93125
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 56733/90513 (63%)
Visitors : 12068430      Online Users : 60
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/987654321/93125


    Title: Online multistep-ahead inundation depth forecasts by recurrent NARX networks
    Authors: Shen, Hung-Yu;Chang, Li-Chiu
    Contributors: 淡江大學水資源及環境工程學系
    Date: 2013-03
    Issue Date: 2013-11-22 12:23:27 (UTC+8)
    Abstract: Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to online multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an online feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make online forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to online multistep-ahead forecasts of inundation depths in the study area during typhoon events.
    Relation: Hydrology and Earth Systems Science 17(3), pp.935-945
    DOI: 10.5194/hess-17-935-2013
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

    Files in This Item:

    File Description SizeFormat
    hess-17-935-2013.pdf1082KbAdobe PDF313View/Open
    index.html0KbHTML183View/Open

    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