A machine code-based genetic programming for suspended sediment concentration estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Kisi2010939,
  author =       "Ozgur Kisi and Aytac Guven",
  title =        "A machine code-based genetic programming for suspended
                 sediment concentration estimation",
  journal =      "Advances in Engineering Software",
  volume =       "41",
  number =       "7-8",
  pages =        "939--945",
  year =         "2010",
  note =         "Advances in Structural Optimization",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2010.06.001",
  URL =          "http://www.sciencedirect.com/science/article/B6V1P-50G0DYF-1/2/c917b48e2cfced4167ad3ab9ee02e797",
  keywords =     "genetic algorithms, genetic programming, Suspended
                 sediment concentration, Modelling, Neuro-fuzzy, Neural
                 networks, Rating curve",
  abstract =     "Correct estimation of suspended sediment concentration
                 carried by a river is very important for many water
                 resources projects. The application of linear genetic
                 programming (LGP), which is an extension to genetic
                 programming (GP) technique, for suspended sediment
                 concentration estimation is proposed in this paper. The
                 LGP is compared with those of the adaptive neuro-fuzzy,
                 neural networks and rating curve models. The daily
                 streamflow and suspended sediment concentration data
                 from two stations, Rio Valenciano Station and Quebrada
                 Blanca Station, operated by the US Geological Survey
                 (USGS) are used as case studies. The root mean square
                 errors (RMSE) and determination coefficient (R2)
                 statistics are used for evaluating the accuracy of the
                 models. Comparison of the results indicated that the
                 LGP performs better than the neuro-fuzzy, neural
                 networks and rating curve models. For the Rio
                 Valenciano and Quebrada Blanca Stations, it is found
                 that the LGP models with RMSE = 44.4 mg/l, R2 = 0.910
                 and RMSE = 13.9 mg/l, R2 = 0.952 in test period is
                 superior in estimating daily suspended sediment
                 concentrations than the best accurate neuro-fuzzy model
                 with RMSE = 52.0 mg/l, R2 = 0.876 and RMSE = 17.9 mg/l,
                 R2 = 0.929, respectively.",
}

Genetic Programming entries for Ozgur Kisi Aytac Guven

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