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    Radial Basis Function Neural Network for Modeling Rating Curves

    Source: Journal of Hydrologic Engineering:;2003:;Volume ( 008 ):;issue: 003
    Author:
    K. P. Sudheer
    ,
    S. K. Jain
    DOI: 10.1061/(ASCE)1084-0699(2003)8:3(161)
    Publisher: American Society of Civil Engineers
    Abstract: The establishment of a rating curve is an important problem in hydrology. Generally, a regression approach is applied to establish the relationship between stage and discharge. However, this approach fails in the cases where hysteresis is present in the data. The aim of the study is to investigate the potential of employing radial basis function (RBF) type neural networks for modeling stage-discharge relationships at gauging stations and to compare different types of networks. The results are promising and suggest that the neural network approach is highly viable. A comparison of the RBF models with backpropagation type neural networks reveals that the former is superior in performance for rating curves exhibiting hysteresis.
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      Radial Basis Function Neural Network for Modeling Rating Curves

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    http://yetl.yabesh.ir/yetl1/handle/yetl/49712
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    contributor authorK. P. Sudheer
    contributor authorS. K. Jain
    date accessioned2017-05-08T21:23:36Z
    date available2017-05-08T21:23:36Z
    date copyrightMay 2003
    date issued2003
    identifier other%28asce%291084-0699%282003%298%3A3%28161%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/49712
    description abstractThe establishment of a rating curve is an important problem in hydrology. Generally, a regression approach is applied to establish the relationship between stage and discharge. However, this approach fails in the cases where hysteresis is present in the data. The aim of the study is to investigate the potential of employing radial basis function (RBF) type neural networks for modeling stage-discharge relationships at gauging stations and to compare different types of networks. The results are promising and suggest that the neural network approach is highly viable. A comparison of the RBF models with backpropagation type neural networks reveals that the former is superior in performance for rating curves exhibiting hysteresis.
    publisherAmerican Society of Civil Engineers
    titleRadial Basis Function Neural Network for Modeling Rating Curves
    typeJournal Paper
    journal volume8
    journal issue3
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)1084-0699(2003)8:3(161)
    treeJournal of Hydrologic Engineering:;2003:;Volume ( 008 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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