Forecasting recruitment and stock biomass of Northeast Arctic cod using neural networks

Authors

  • Geir Huse Institute of Marine Research
  • Geir Ottersen Division of Zoology, Department of Biology, University of Oslo

DOI:

https://doi.org/10.3989/scimar.2003.67s1325

Keywords:

cod, forecasting, neural networks, genetic algorithm, recruitment, abundance, temperature, growth, population dynamics

Abstract


We apply an artificial neural network (ANN) to predict recruitment and biomass development of Northeast Arctic cod. The ANN is trained using a genetic algorithm with input time series such as spawning stock biomass of cod, herring and capelin biomass, and temperature. Forecasts were made by training the ANN on parts of the time series (training set), and then using a trained ANN to predict cod recruitment or biomass in years outside of the training set. In general the predictions corresponded well to observations. The correlation (r2) between observed and predicted stock recruitment at age 3 was 0.74, based on a model with temperature, spawning stock biomass, and capelin biomass. The correlation between observed and predicted stock biomass was 0.89, 0.72 and 0.57 for one, two and three year predictions respectively. The best model for the one year predictions was based on input information on cod biomass, temperature, and cod landings. These results illustrate the strong forecasting ability of ANN models. In the light of our findings we discuss the potential benefit of applying ANN models as a forecasting technology in fisheries assessment.

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Published

2003-04-30

How to Cite

1.
Huse G, Ottersen G. Forecasting recruitment and stock biomass of Northeast Arctic cod using neural networks. scimar [Internet]. 2003Apr.30 [cited 2023Apr.1];67(S1):325-3. Available from: https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/532

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Section

Articles