Regression model for sediment transport problems using multi-gene symbolic genetic programming

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@Article{Kumar:2014:CEA,
  author =       "Bimlesh Kumar and Anjaneya Jha and 
                 Vishal Deshpande and Gopu Sreenivasulu",
  title =        "Regression model for sediment transport problems using
                 multi-gene symbolic genetic programming",
  journal =      "Computers and Electronics in Agriculture",
  volume =       "103",
  pages =        "82--90",
  year =         "2014",
  ISSN =         "0168-1699",
  DOI =          "doi:10.1016/j.compag.2014.02.010",
  URL =          "http://www.sciencedirect.com/science/article/pii/S016816991400057X",
  keywords =     "genetic algorithms, genetic programming, Incipient
                 motion, Sediment transport, Total bed load, Vegetated
                 flow",
  abstract =     "Sediment transport modelling problems are complex due
                 to the multi-dimensionality of the problems, along with
                 their nonlinear interdependence. Also, in river
                 hydraulics, phenomena are stochastic and variables are
                 measured with uncertainties which are unavoidable.
                 Dimensional and regression analyses have been employed
                 in the past but have associated limitations. As a
                 robust modelling tool, genetic programming was used to
                 develop predictor models for three different but
                 related problems of sediment transport-vegetated flow,
                 incipient motion and total bed load prediction. A
                 relatively new development over the conventional
                 genetic programming-multi-gene symbolic regression was
                 used to model functional relationships that were able
                 to generality's highly nonlinear variations in data as
                 well as predict system behaved from independent input
                 data in all the three cases. The algorithmic parameters
                 for genetic programming technique were resolved
                 iteratively, varying based on problems in context. For
                 all the three models developed, model efficiency
                 criteria were found out and presented and the
                 performance of the present model was compared with
                 several past models for the same data points. The
                 models developed herein were able to generalize the
                 underlying relationships in the presented data as well
                 as were able to predict values for unknown data with
                 high accuracy.",
}

Genetic Programming entries for Bimlesh Kumar Anjaneya Jha Vishal Deshpande Gopu Sreenivasulu

Citations