Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/68709
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Type: Conference paper
Title: Using Multi-Layer Perceptrons to Predict the Presence of Jellyfish of the Genus Physalia at New Zealand Beaches
Author: Pontin, D.
Watts, M.
Worner, S.
Citation: IEEE International Joint Conference on Neural Networks, 2008; pp.1171-1175
Publisher: IEEE
Publisher Place: New York
Issue Date: 2008
ISBN: 9781424418213
Conference Name: IEEE International Joint Conference on Neural Networks (2008 : Hong Kong)
Statement of
Responsibility: 
David R. Pontin, Michael J. Watts and S. P. Worner
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.
Rights: © 2008 IEEE
RMID: 0020103718
DOI: 10.1109/IJCNN.2008.4633947
Appears in Collections:Earth and Environmental Sciences publications
Environment Institute publications

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