Analysis and prediction of the fluctuation of sardine abundance using a neural network
1997
Aoki, I | Komatsu, T
This paper examines the use of a neural network to analyse and predict the winter catch, in the Joban-Boso Seas off the Pacific coast of central Japan, of young Japanese sardine (Sardinops melanostictus) representing the index of recruits in the sardine stock. The supervised learning paradigm, a three-layer network and a back-propagation algorithm were employed in constructing the neural net. A number of biological, hydrographic and climatic factors constituted an input vector, the output being the catch of young sardine. The association of sardine abundance with environmental factors was quantified in the form of the trained neural network, which indicated important associations with the Southern Oscillation Index, with patterns of the Kuroshio and the Oyashio currents, and with SST and zooplankton densities in the northeastern sea area of Japan. Changes in the sardine abundance during the past two decades could be predicted from environmental conditions. Neural networks can serve as a useful tool for the analysis and prediction of such marine ecosystem dynamics including fish population dynamics which are typically non linear multivariate problems.
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