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|Title:||Using Multi-Layer Perceptrons to Predict the Presence of Jellyfish of the Genus Physalia at New Zealand Beaches|
|Citation:||IEEE International Joint Conference on Neural Networks, 2008; pp.1171-1175|
|Publisher Place:||New York|
|Conference Name:||IEEE International Joint Conference on Neural Networks (2008 : Hong Kong)|
|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|
|Appears in Collections:||Earth and Environmental Sciences publications|
Environment Institute publications
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