• Login
    View Item 
    •   DSpace Home
    • Institute's contributions
    • Scholarly literature
    • View Item
    •   DSpace Home
    • Institute's contributions
    • Scholarly literature
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Prediction of littoral drift with artificial neural networks

    Thumbnail
    View/Open
    Hydrol_Earth_Syst_Sci_12_267.pdf (355.4Kb)
    Date
    2008
    Author
    Singh, A.K.
    Deo, M.C.
    SanilKumar, V.
    Metadata
    Show full item record
    Abstract
    The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past an accurate and reliable estimation of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables. The best network was selected after trying out many alternatives. In order to improve the accuracy further its outcome was used to develop another network. Such simple two-stage training yielded most satisfactory results. An equation combining the network and a non-linear regression is presented for quick field usage. An attempt was made to see how both ANN and statistical regression differ in processing the input information. The network was validated by confirming its consistency with underlying physical process
    URI
    http://drs.nio.org/drs/handle/2264/1041
    Collections
    • Scholarly literature [9074]

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV