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    Integration of Artificial Neural Networks with Radial Basis Function Interpolation in Earthfill Dam Seepage Modeling

    Source: Journal of Computing in Civil Engineering:;2013:;Volume ( 027 ):;issue: 002
    Author:
    Vahid Nourani
    ,
    Ali Babakhani
    DOI: 10.1061/(ASCE)CP.1943-5487.0000200
    Publisher: American Society of Civil Engineers
    Abstract: In this study, the radial basis function (RBF) spatial interpolation method was used to estimate the potential water heads through an earthen dam. The multiquadric (MQ) function was used to discretize the seepage governing partial differential equation and related boundary conditions. The function contains a shape coefficient of
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      Integration of Artificial Neural Networks with Radial Basis Function Interpolation in Earthfill Dam Seepage Modeling

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    http://yetl.yabesh.ir/yetl1/handle/yetl/59179
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    contributor authorVahid Nourani
    contributor authorAli Babakhani
    date accessioned2017-05-08T21:40:36Z
    date available2017-05-08T21:40:36Z
    date copyrightMarch 2013
    date issued2013
    identifier other%28asce%29cp%2E1943-5487%2E0000207.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/59179
    description abstractIn this study, the radial basis function (RBF) spatial interpolation method was used to estimate the potential water heads through an earthen dam. The multiquadric (MQ) function was used to discretize the seepage governing partial differential equation and related boundary conditions. The function contains a shape coefficient of
    publisherAmerican Society of Civil Engineers
    titleIntegration of Artificial Neural Networks with Radial Basis Function Interpolation in Earthfill Dam Seepage Modeling
    typeJournal Paper
    journal volume27
    journal issue2
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000200
    treeJournal of Computing in Civil Engineering:;2013:;Volume ( 027 ):;issue: 002
    contenttypeFulltext
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