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.
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