Application of a genetic algorithm in predicting the percentage of shear force carried by walls in smooth rectangular channels

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@Article{SheikhKhozani:2016:Measurement,
  author =       "Zohreh Sheikh Khozani and Hossein Bonakdari and 
                 Amir Hossein Zaji",
  title =        "Application of a genetic algorithm in predicting the
                 percentage of shear force carried by walls in smooth
                 rectangular channels",
  journal =      "Measurement",
  volume =       "87",
  pages =        "87--98",
  year =         "2016",
  ISSN =         "0263-2241",
  DOI =          "doi:10.1016/j.measurement.2016.03.018",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0263224116001810",
  abstract =     "Shear stress comprises basic information for
                 predicting average depth velocity and discharge in
                 channels. With knowledge of the percentage of shear
                 force carried by walls (%SFw) it is possible to more
                 accurately estimate shear stress values. The percentSFw
                 in smooth rectangular channels was predicted by
                 extending two soft computing methods: Genetic Algorithm
                 Artificial (GAA) neural network and Genetic Programming
                 (GP). In order to investigate the percentage of shear
                 force, 8 data series with a total of 69 different data
                 were used. The outcomes of the GAA model (an equation)
                 and the GP model (a program) were presented. In order
                 to detect these models' ability to predict percentSFw,
                 the obtained results were compared with several
                 equations derived by other researchers. The GAA model
                 with RMSE of 2.5454 and the GP model with RMSE of
                 3.0559 performed better than other equations with mean
                 RMSE of about 9.630.",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural network, Genetic programing, Average shear
                 force, Rectangular channel",
  notes =        "Department of Civil Engineering, Razi University,
                 Kermanshah, Iran",
}

Genetic Programming entries for Zohreh Sheikh Khozani Hossein Bonakdari Amir Hossein Zaji

Citations