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    Using multi-layer perceptrons to predict the presence of jellyfish of the genus Physalia at New Zealand beaches

    Pontin, David R.; Watts, Michael J.; Worner, Susan P.
    Abstract
    The apparent increase in number and magnitude of jellyfish blooms in the world's 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.... [Show full abstract]
    Keywords
    multilayer perceptron; Physalia; jellyfish; beaches; New Zealand; oceanographic data; artificial neural networks
    Date
    2008-06
    Type
    Conference Contribution - Published (Conference Paper)
    Collections
    • Department of Pest Management and Conservation [646]
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    IJCNN.2008.4633947.pdf
    DOI
    https://doi.org/10.1109/IJCNN.2008.4633947
    Metadata
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    © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Citation
    Pontin, D. R., Watts, M. J., & Worner, S. P. (2008). Using multi-layer perceptrons to predict the presence of jellyfish of the genus Physalia at New Zealand beaches. In IEEE International Joint Conference on Neural Networks, 2008 (pp. 1170-1175). Piscataway, NJ: IEEE.
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