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
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