A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River

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@Article{Olyaie:2016:GF,
  author =       "Ehsan Olyaie and Hamid Zare Abyaneh and 
                 Ali Danandeh Mehr",
  title =        "A comparative analysis among computational
                 intelligence techniques for dissolved oxygen prediction
                 in Delaware River",
  journal =      "Geoscience Frontiers",
  year =         "2016",
  ISSN =         "1674-9871",
  DOI =          "doi:10.1016/j.gsf.2016.04.007",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1674987116300469",
  abstract =     "Most of the water quality models previously developed
                 and used in dissolved oxygen (DO) prediction are
                 complex. Moreover, reliable data available to
                 develop/calibrate new DO models is scarce. Therefore,
                 there is a need to study and develop models that can
                 handle easily measurable parameters of a particular
                 site, even with short length. In recent decades,
                 computational intelligence techniques, as effective
                 approaches for predicting complicated and significant
                 indicator of the state of aquatic ecosystems such as
                 DO, have created a great change in predictions. In this
                 study, three different AI methods comprising: (1) two
                 types of artificial neural networks (ANN) namely multi
                 linear perceptron (MLP) and radial based function
                 (RBF); (2) an advancement of genetic programming namely
                 linear genetic programming (LGP); and (3) a support
                 vector machine (SVM) technique were used for DO
                 prediction in Delaware River located at Trenton, USA.
                 For evaluating the performance of the proposed models,
                 root mean square error (RMSE), Nash-Sutcliffe
                 efficiency coefficient (NS), mean absolute relative
                 error (MARE) and, correlation coefficient statistics
                 (R) were used to choose the best predictive model. The
                 comparison of estimation accuracies of various
                 intelligence models illustrated that the SVM was able
                 to develop the most accurate model in DO estimation in
                 comparison to other models. Also, it was found that the
                 LGP model performs better than the both ANNs models.
                 For example, the determination coefficient was 0.99 for
                 the best SVM model, while it was 0.96, 0.91 and 0.81
                 for the best LGP, MLP and RBF models, respectively. In
                 general, the results indicated that an SVM model could
                 be employed satisfactorily in DO estimation.",
  keywords =     "genetic algorithms, genetic programming, Dissolved
                 Oxygen, SVM, LGP, ANN, modelling",
}

Genetic Programming entries for Ehsan Olyaie Hamid Zare Abyaneh Ali Danandeh Mehr

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