淡江大學機構典藏:Item 987654321/116056
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 56732/90513 (63%)
造访人次 : 12068417      在线人数 : 52
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/987654321/116056


    题名: Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques
    作者: Chang, Fi-John;Chen, Pin-An;Chang, Li-Chiu;Tsai, Yu-Hsuan
    关键词: Total phosphate (TP);Water qualityArtificial neural network (ANN);Nonlinear autoregressive with eXogenous input (NARX);networkGamma test
    日期: 2016-08-15
    上传时间: 2019-03-23 12:11:13 (UTC+8)
    摘要: This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN—static neural network; NARX network—dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest.
    關聯: Science of The Total Environment 562, p.228-236
    DOI: 10.1016/j.scitotenv.2016.03.219
    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
    Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.pdf1637KbAdobe PDF0检视/开启
    index.html0KbHTML13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