English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 49378/84106 (59%)
造访人次 : 7368166      在线人数 : 65
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/44555


    题名: The strategy of building a flood forecast model by neuro-fuzzy network
    作者: Chen, Shen-hsien;Lin, Yong-huang;張麗秋;Chang, Li-chiu;Chang, Fi-john
    贡献者: 淡江大學水資源及環境工程學系
    关键词: flood forecast;neuro-fuzzy;artificial neural network;BPNN;ANFIS
    日期: 2006-04
    上传时间: 2010-03-26 16:17:59 (UTC+8)
    出版者: Bognor Regis: John Wiley & Sons Ltd.
    摘要: A methodology is proposed for constructing a flood forecast model using the adaptive neuro-fuzzy inference system (ANFIS). This is based on a self-organizing rule-base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall-runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self-constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back-propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model.
    關聯: Hydrological processes 20(7), pp.1525-1540
    DOI: 10.1002/hyp.5942
    显示于类别:[水資源及環境工程學系暨研究所] 期刊論文

    文件中的档案:

    档案 大小格式浏览次数
    index.html0KbHTML225检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

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