Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions

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@Article{Zaji:2015:FMI,
  author =       "Amir Hossein Zaji and Hossein Bonakdari",
  title =        "Application of artificial neural network and genetic
                 programming models for estimating the longitudinal
                 velocity field in open channel junctions",
  journal =      "Flow Measurement and Instrumentation",
  volume =       "41",
  pages =        "81--89",
  year =         "2015",
  ISSN =         "0955-5986",
  DOI =          "doi:10.1016/j.flowmeasinst.2014.10.011",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0955598614001307",
  abstract =     "Estimating the accurate longitudinal velocity fields
                 in an open channel junction has a great impact on
                 hydraulic structures such as irrigation and drainage
                 channels, river systems and sewer networks. In this
                 study, Genetic Programming (GP) and Multi-Layer
                 Perceptron Artificial Neural Network (MLP-ANN) were
                 modelled and compared to find an analytical formulation
                 that could present a continuous spatial description of
                 velocity in open channel junction by using discrete
                 information of laboratory measurements. Three direction
                 coordinates of each point of the fluid flow and
                 discharge ratio of main to tributary channel were used
                 as inputs to the GP and ANN models. The training and
                 testing of the models were performed according to the
                 published experimental data from the related
                 literature. To find the accurate prediction ability of
                 GP and ANN models in cases with minor training dataset,
                 the models were compared with various percents of
                 allocated data to train dataset. New formulations were
                 obtained from GP and ANN models that can be applied for
                 practical longitudinal velocity field prediction in an
                 open channel junction. The results showed that ANN
                 model by Root Mean Squared Error (RMSE) of 0.068
                 performs better than GP model by RMSE of 0.162, and
                 that ANN can model the longitudinal velocity field with
                 small population of train dataset with high accuracy.",
  keywords =     "genetic algorithms, genetic programming, Open channel
                 junction, Artificial neural network, Longitudinal
                 velocity fields",
}

Genetic Programming entries for Amir Hossein Zaji Hossein Bonakdari

Citations