Multigene genetic programming for sediment transport modeling in sewers for conditions of non-deposition with a bed deposit

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@Article{SAFARI2018,
  author =       "Mir Jafar Sadegh Safari and Ali {Danandeh Mehr}",
  title =        "Multigene genetic programming for sediment transport
                 modeling in sewers for conditions of non-deposition
                 with a bed deposit",
  journal =      "International Journal of Sediment Research",
  year =         "2018",
  volume =       "33",
  number =       "3",
  pages =        "262--270",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Multigene
                 genetic programming, Bed load, Bed deposition,
                 Non-deposition, Sediment transport, Sewer",
  ISSN =         "1001-6279",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1001627917300252",
  DOI =          "doi:10.1016/j.ijsrc.2018.04.007",
  abstract =     "It is known that construction of large sewers based on
                 consideration of flow with non-deposition without a bed
                 deposit is not economical. Sewer design based on
                 consideration of flow with non-deposition with a bed
                 deposit reduces channel bed slope and construction cost
                 in which the presence of a small depth of sediment
                 deposition on the bed increases the sediment transport
                 capacity of the flow. This paper suggests a new
                 Pareto-optimal model developed by the multigene genetic
                 programming (MGGP) technique to estimate particle
                 Froude number (Frp) in large sewers with conditions of
                 sediment deposition on the bed. To this end, four data
                 sets including wide ranges of sediment size and
                 concentration, deposit thickness, and pipe size are
                 used. On the basis of different statistical performance
                 indices, the efficiency of the proposed Pareto-optimal
                 MGGP model is compared to those of the best MGGP model
                 developed in the current study as well as the
                 conventional regression models available in the
                 literature. The results indicate the higher efficiency
                 of the MGGP-based models for Frp estimation in the case
                 of no additional deposition onto a bed with a sediment
                 deposit. Inasmuch as the Pareto-optimal MGGP model uses
                 a lower number of input parameters to yield
                 comparatively higher performance than the conventional
                 regression models, it can be used as a parsimonious
                 model for self-cleansing design of large sewers in
                 practice.",
}

Genetic Programming entries for Mir Jafar Sadegh Safari Ali Danandeh Mehr

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