• DocumentCode
    2956613
  • Title

    Using Multi-Layer Perceptrons to predict the presence of jellyfish of the genus Physalia at New Zealand beaches

  • Author

    Pontin, David R. ; Watts, Michael J. ; Worner, S.P.

  • Author_Institution
    Bio-Protection & Ecology Div., Lincoln Univ., Lincoln
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    1170
  • Lastpage
    1175
  • Abstract
    The apparent increase in number and magnitude of jellyfish blooms in the worlds oceans has lead to concerns over potential disruption and harm to global fishery stocks. Because of the potential harm that jellyfish populations can cause and to avoid impact it would be helpful to model jellyfish populations so that species presence or absence can be predicted. Data on the presence or absence of jellyfish of the genus Physalia was modelled using multi-layer perceptrons (MLP) based on oceanographic data. Results indicated that MLP are capable of predicting the presence or absence of Physalia in two regions in New Zealand and of identifying significant biological variables.
  • Keywords
    geography; multilayer perceptrons; oceanographic techniques; New Zealand beaches; Physalia beaches; biological variables; global fishery stocks; jellyfish; multilayer perceptrons; oceanographic data; Aquaculture; Artificial neural networks; Availability; Biological system modeling; Ecosystems; Environmental factors; Frequency; Multilayer perceptrons; Oceans; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4633947
  • Filename
    4633947