Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches

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@Article{Kisi20123454,

• author = "Ozgur Kisi and M. Emin Emiroglu and Omer Bilhan and Aytac Guven",
• title = "Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches",
• journal = "Expert Systems with Applications",
• volume = "39",
• number = "3",
• pages = "3454--3460",
• year = "2012",
• ISSN = "0957-4174",
• DOI = "doi:10.1016/j.eswa.2011.09.035",
• URL = "http://www.sciencedirect.com/science/article/pii/S0957417411013443",
• keywords = "genetic algorithms, genetic programming, gene expression programming, Side weir, Discharge coefficient, Intake, Triangular weir, Labyrinth, Neural networks",
• abstract = "This paper presents the results of laboratory model testing of triangular labyrinth side weirs located on the straight open channel flume. The discharge capacity of triangular labyrinth side weirs is estimated by using two different artificial neural network (ANN) techniques, that is, the radial basis neural network (RBNN) and generalised regression neural network (GRNN), and gene-expression programming (GEP), which is an extension to genetic programming. 2500 laboratory test results are used for determining discharge coefficient of triangular labyrinth side weirs. The performance of the ANN and GEP models is compared with multi-linear and nonlinear regression models. Comparison results indicated that the neural computing and gene-expression programming techniques could be employed successfully in modelling discharge coefficient from the available experimental data.",

}

Genetic Programming entries for Ozgur Kisi M Emin Emiroglu Omer Bilhan Aytac Guven

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