Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois

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@Article{Markus:2010:JH,
  author =       "Momcilo Markus and Mohamad I. Hejazi and 
                 Peter Bajcsy and Orazio Giustolisi and Dragan A. Savic",
  title =        "Prediction of weekly {nitrate-N} fluctuations in a
                 small agricultural watershed in {Illinois}",
  journal =      "Journal of Hydroinformatics",
  year =         "2010",
  volume =       "12",
  number =       "3",
  pages =        "251--261",
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural networks, drinking water, forecasting, naive
                 Bayes model, nitrate-N",
  ISSN =         "1464-7141",
  DOI =          "doi:10.2166/hydro.2010.064",
  size =         "11 pages",
  publisher =    "IWA Publishing",
  abstract =     "Agricultural nonpoint source pollution has been
                 identified as one of the leading causes of surface
                 water quality impairment in the United States. Such an
                 impact is important, particularly in predominantly
                 agricultural areas, where application of agricultural
                 fertilisers often results in excessive nitrate levels
                 in streams and rivers. When nitrate concentration in a
                 public water supply reaches or exceeds drinking water
                 standards, costly measures such as well closure or
                 water treatment have to be considered. Thus, having
                 accurate nitrate-N predictions is critical in making
                 correct and timely management decisions. This study
                 applied a set of data mining tools to predict weekly
                 nitrate-N concentrations at a gauging station on the
                 Sangamon River near Decatur, Illinois, USA. The data
                 mining tools used in this study included artificial
                 neural networks, evolutionary polynomial regression and
                 the naive Bayes model. The results were compared using
                 seven forecast measures. In general, all models
                 performed reasonably well, but not all achieved best
                 scores in each of the measures, suggesting that a
                 multi-tool approach is needed. In addition to improving
                 forecast accuracy compared with previous studies, the
                 tools described in this study demonstrated potential
                 for application in error analysis, input selection and
                 ranking of explanatory variables, thereby designing
                 cost-effective monitoring networks.",
  notes =        "Institute of Natural Resource Sustainability,
                 University of Illinois at Urbana-Champaign, 2204
                 Griffith Dr, Champaign, Illinois 61820, USA E-mail:
                 mmarkus@illinois.edu Ven-Te Chow Hydrosystems
                 Laboratory, Department of Civil and Environmental
                 Engineering, University of Illinois at
                 Urbana-Champaign, 205 North Mathews Ave, Urbana, IL
                 61801, USA National Center for Supercomputing
                 Applications, University of Illinois at
                 Urbana-Champaign, 1205 West Clark Street, Urbana, IL
                 61801, USA Department of Civil and Environmental
                 Engineering, Technical University of Bari, II
                 Engineering Faculty, Taranto via Turismo 8, 74100,
                 Italy Centre for Water Systems, School of Engineering,
                 Computing and Mathematics, University of Exeter,
                 Harrison Building, North Park Road, Exeter EX4 4QF,
                 UK",
}

Genetic Programming entries for Momcilo Markus Mohamad I Hejazi Peter Bajcsy Orazio Giustolisi Dragan Savic

Citations