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  • Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers

    Author(s)
    Roushangar, Kiyoumars
    Koosheh, Ali
    Griffith University Author(s)
    Koosheh, Ali
    Year published
    2015
    Metadata
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    Abstract
    The aim of the present study is to apply Support Vector Regression (SVR) method to predict bed load transport rates for three gravel-bed rivers. Different combinations of hydraulic parameters are used as inputs for modeling bed load transport using four kernel functions of SVR models. Genetic Algorithm (GA) method is applicably administered to determine optimal SVR parameters. The GA-SVR models are developed and tested using the available data sets, and consecutive predicted results are compared in terms of Efficiency Coefficient and Correlation Coefficient. Obtained results show that the GA-SVR models with Exponential Radial ...
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    The aim of the present study is to apply Support Vector Regression (SVR) method to predict bed load transport rates for three gravel-bed rivers. Different combinations of hydraulic parameters are used as inputs for modeling bed load transport using four kernel functions of SVR models. Genetic Algorithm (GA) method is applicably administered to determine optimal SVR parameters. The GA-SVR models are developed and tested using the available data sets, and consecutive predicted results are compared in terms of Efficiency Coefficient and Correlation Coefficient. Obtained results show that the GA-SVR models with Exponential Radial Basis Function (ERBF) kernel present higher accuracy than the other applied GA-SVR models. Furthermore, testing data sets are predicted by Einstein and Meyer-Peter and Müller (MPM) formulas. The GA-SVR models demonstrate a better performance compared to the traditional bed load formulas. Finally, high bed load transport values were eliminated from data sets and the models are re-analyzed. The elimination of high bed load transport rates improves prediction accuracy using GA-SVR method.
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    Journal Title
    Journal of Hydrology
    Volume
    527
    DOI
    https://doi.org/10.1016/j.jhydrol.2015.06.006
    Subject
    Science & Technology
    Physical Sciences
    Engineering, Civil
    Geosciences, Multidisciplinary
    Publication URI
    http://hdl.handle.net/10072/412175
    Collection
    • Journal articles

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