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
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;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4633947