Modelling Streamflow-Sediment Relationship Using Genetic Programming

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

@InProceedings{Jaiyeola:2015:LENFI,
  author =       "Adesoji Tunbosun Jaiyeola",
  title =        "Modelling Streamflow-Sediment Relationship Using
                 Genetic Programming",
  booktitle =    "Advances in Energy and Environmental Science and
                 Engineering",
  year =         "2015",
  editor =       "Aida Bulucea",
  volume =       "41",
  series =       "Energy, Environmental and Structural Engineering
                 Series",
  pages =        "124--129",
  address =      "Michigan State University, East Lansing, MI, USA",
  month =        sep # " 20-22",
  publisher =    "WSEAS",
  keywords =     "genetic algorithms, genetic programming, Streamflow,
                 suspended sediment, GPdotNET, data-driven modelling",
  isbn13 =       "978-1-61804-338-2",
  URL =          "http://www.wseas.us/e-library/conferences/2015/Michigan/LENFI/LENFI-18.pdf",
  size =         "6 pages",
  abstract =     "The presence of sediment in a river or reservoir is
                 detrimental to the operation and management of water
                 resources because it affects the design, planning and
                 management of any water resource. Hence it is important
                 to accurately estimate the quantity of sediment flowing
                 in a river or been transported into a reservoir. The
                 process of measuring the quantity of sediment in a
                 river manually or using automatic sampling device is
                 labour intensive, expensive and time consuming. In this
                 study a data-driven approach, genetic programming
                 techniques is used to develop an explicit model that
                 accurately captures the relationship between streamflow
                 and suspended sediment. The accuracy of the developed
                 models was evaluated using Root Mean Square Error
                 (RMSE) and Determination Coefficient (R2). The results
                 show that GP is capable of modelling streamflow
                 sediment process accurately with R-squared value of
                 0.999 and RMS errors of 0.032 during the validation
                 phase.",
  notes =        "Mangosuthu University of Technology, Durban",
}

Genetic Programming entries for Adesoji Tunbosun Jaiyeola

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