English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 62822/95882 (66%)
造访人次 : 4027689      在线人数 : 1143
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/100121


    题名: Regional flood inundation nowcast using hybrid SOM and dynamic neural networks
    作者: Chang, Li-Chiu;Sheng, Hung-Yu;Chang, Fi-John
    贡献者: 淡江大學水資源及環境工程學系
    关键词: Artificial neural network (ANN);Self-organizing map (SOM);Recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX);Flood inundation map;Regional flood forecasting model
    日期: 2014-11-27
    上传时间: 2015-01-28 11:08:12 (UTC+8)
    出版者: Netherlands: Elsevier BV
    摘要: This study proposes a hybrid SOM–R-NARX methodology for nowcasting multi-step-ahead regional flood inundation maps during typhoon events. The core idea is to form a meaningful topology of inundation maps and then real-time update the selected inundation map according to a forecasted total inundated volume. The methodology includes three major schemes: (1) configuring the self-organizing map (SOM) to categorize a large number of regional inundation maps into a meaningful topology; (2) building a recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX) to forecast the total inundated volume; and (3) adjusting the weights of the selected neuron in the constructed SOM based on the forecasted total inundated volume to obtain a real-time adapted regional inundation map. The proposed models are trained and tested based on a large number of inundation data sets collected in an inundation-prone region (270 km2) in the Yilan County, Taiwan. The results show that (1) the SOM–R-NARX model can suitably forecast multi-step-ahead regional inundation maps; and (2) the SOM–R-NARX model consistently outperforms the comparative model in providing regional inundation maps with smaller forecast errors and higher correlation (RMSE < 0.1 m and R2 > 0.9 in most cases). The proposed modelling approach offers an insightful and promising methodology for real-time forecasting 2-dimensional visible inundation maps during storm events.
    關聯: Journal of Hydrology 519(pt.A), pp.476-489
    DOI: 10.1016/j.jhydrol.2014.07.036
    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 描述 大小格式浏览次数
    index.html0KbHTML311检视/开启
    Regional flood inundation nowcast using hybrid SOM and dynamic neural networks.pdf3988KbAdobe PDF1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