Using Time Lagged Input Data to Improve Prediction of Stinging Jellyfish Occurrence at New Zealand Beaches by Multi-Layer Perceptrons
Environmental changes in oceanic conditions have the potential to cause jellyfish populations to rapidly expand leading to ecosystem level repercussions. To predict potential changes it is necessary to understand how such populations are influenced by oceanographic conditions. Data recording the presence or absence of jellyfish of the genus Physalia at beaches in the West Auckland region of New Zealand were modelled using Multi-Layer Perceptrons (MLP) with time lagged oceanographic data as input data. Results showed that MLP models were able to generalise well based on Kappa statistics and gave good predictions of the presence or absence of Physalia. Moreover, an analysis of the network contributions indicated an interaction between wave and wind variables at different time intervals can promote or inhibit the occurrence of Physalia.
KeywordsWind Direction Date Index Significant Wave Height Window Length Oceanographic Data
Unable to display preview. Download preview PDF.
- 9.Barnes, R.: Invertebrate Zoology. Saunders College, Philadelphia (1980)Google Scholar
- 10.Totton, A.K.: Studies on Physalia physalis: Natural history and morphology. Discovery Reports 30, 301–368 (1960)Google Scholar
- 11.Pontin, D.R., Watts, M.J., Worner, S.P.: Using Multi-Layer Perceptrons to Predict the Presence of Jellyfish of the Genus Physalia at New Zealand Beaches. In: International Joint Conference on Neural Networks, Hong Kong, pp. 1171–1176 (2008)Google Scholar
- 12.Tolman, H.L.: Validation of a new global wave forecast system at NCEP. In: Edge, B.L., Helmsley, J.M. (eds.) Ocean Wave Measurements and Analysis. ASCE, pp. 777–786 (1998)Google Scholar
- 13.Flexer, A.: Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice. In: Trappl, R. (ed.) Cybernetics and Systems 1996, Proceedings of the 13th European Meeting on Cybernetics and Systems Research. Austrian Society for Cybernetic Studies, pp. 1005–1008 (1996)Google Scholar
- 16.Olden, J.D., Jackson, D.A.: Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling 154, 135–150 (2002)Google Scholar