Modeling energy dissipation over stepped spillways using machine learning approaches

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@Article{Roushangar:2014:JH,

• author = "Kiyoumars Roushangar and Samira Akhgar and Farzin Salmasi and Jalal Shiri",
• title = "Modeling energy dissipation over stepped spillways using machine learning approaches",
• journal = "Journal of Hydrology",
• year = "2014",
• volume = "508",
• pages = "254--265",
• URL = "http://www.sciencedirect.com/science/article/pii/S002216941300797X",
• keywords = "genetic algorithms, genetic programming, Energy dissipation, Neural network, Sensitivity analysis",
• ISSN = "0022-1694",
• DOI = "10.1016/j.jhydrol.2013.10.053",
• abstract = "Study of the energy dissipation over the stepped spillways is necessary in flood control-related studies. The aim of this study is to apply different methods to modeling energy dissipation in nappe and skimming flow regimes over stepped spillway by using original experimental dataset through the artificial networks (ANNs) and Genetic Expression Programming (GEP) techniques. Subsequently, three kinds of data including the napped and skimming regimes data as well as combination of them are applied as models input–output variables. A preliminary investigation on various GEP operators is also carried out for selecting the proper operators. The obtained results indicate that applied machine learning techniques have reliable performance in predicting energy dissipation over stepped spillways.",
• notes = "Also known as \cite{ROUSHANGAR2014254}

  University of Tabriz, Tabriz, Iran",

}

Genetic Programming entries for Kiyoumars Roushangar Samira Akhgar Farzin Salmasi Jalal Shiri

Citations