Neural network and genetic programming for modelling coastal algal blooms

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{oai:inderscience.com:11208,
  title =        "Neural network and genetic programming for modelling
                 coastal algal blooms",
  author =       "Nitin Muttil and Kwok-Wing Chau",
  journal =      "International Journal of Environment and Pollution",
  year =         "2006",
  volume =       "28",
  pages =        "223--238",
  number =       "3/4",
  month =        "6 " # nov,
  publisher =    "Inderscience Publishers",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  language =     "eng",
  oai =          "oai:inderscience.com:11208",
  relation =     "ISSN online: 1741-5101 ISSN print: 0957-4352 DOI:
                 10.1504/06.11208",
  rights =       "Inderscience Copyright",
  source =       "IJEP (2006), Vol 28 Issue 3/4, pp 223 - 238",
  keywords =     "genetic algorithms, genetic programming, harmful algal
                 blooms, machine learning techniques, artificial neural
                 networks, water quality modelling, Hong Kong, algal
                 biomass, environmental pollution, simulation",
  ISSN =         "1741-5101",
  URL =          "http://www.inderscience.com/link.php?id=11208",
  DOI =          "doi:10.1504/IJEP.2006.011208",
  abstract =     "In the recent past, machine learning (ML) techniques
                 such as artificial neural networks (ANN) have been
                 increasingly used to model algal bloom dynamics. In the
                 present paper, along with ANN, we select genetic
                 programming (GP) for modelling and prediction of algal
                 blooms in Tolo Harbour, Hong Kong. The study of the
                 weights of the trained ANN and also the GP-evolved
                 equations shows that they correctly identify the
                 ecologically significant variables. Analysis of various
                 ANN and GP scenarios indicates that good predictions of
                 long-term trends in algal biomass can be obtained using
                 only chlorophyll-a as input. The results indicate that
                 the use of biweekly data can simulate long-term trends
                 of algal biomass reasonably well, but it is not ideally
                 suited to give short-term algal bloom predictions.",
}

Genetic Programming entries for Nitin Muttil Kwok-Wing Chau

Citations