English  |  正體中文  |  简体中文  |  Items with full text/Total items : 65318/65318 (100%)
Visitors : 21749237      Online Users : 196
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU 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://ir.lib.ncu.edu.tw/handle/987654321/50586


    Title: Development of an artificial neural network model for determination of longitudinal and transverse dispersivities in a convergent flow tracer test
    Authors: Shieh,HY;Chen,JS;Lin,CN;Wang,WK;Liu,CW
    Contributors: 應用地質研究所
    Keywords: SCALE-DEPENDENT DISPERSION;AQUIFER PARAMETERS;FIELD;SYSTEMS
    Date: 2010
    Issue Date: 2012-03-27 17:37:21 (UTC+8)
    Publisher: 國立中央大學
    Abstract: The convergent flow tracer test is an efficient method for determining dispersivity in field, but the traditional curve-fitting method for the estimation of dispersivity from a convergent flow tracer test is quite time-consuming. In this study, we present a model to improve the evaluation of longitudinal and transverse dispersivities from a convergent flow tracer test which couples a back-propagation neural network (BPN) model with a two-dimensional convergent flow tracer transport model. The prediction errors for the training and validation data show that with the effective porosity fitting model, the scale-dependent longitudinal dispersivity fitting model, and the scale-dependent transverse dispersivity fitting model, we can obtain satisfactory prediction accuracy with much less computational time. The applicable ranges of parameters are: The Peclet number is between 0.5 and 100, the effective porosity is between 0.05 and 0.5 and the scale-dependent transverse dispersivity is between 0.01 and 10 m. One set of hypothetical data and one set of field data are used to demonstrate the robustness and accuracy of the back-propagation neural network fitting model (BPNFM). The results demonstrate that BPNFM has the advantage of significantly saving the computational time and giving more accurate transport parameter values. The developed BPNFM is an effective tool for fast and accurate evaluation of the longitudinal and transverse dispersivities for a field convergent flow tracer test. (c) 2010 Elsevier B.V. All rights reserved.
    Relation: JOURNAL OF HYDROLOGY
    Appears in Collections:[應用地質研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML499View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明