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Forecasting recruitment and stock biomass of northeast Arctic cod using neural networks
Huse, G.; Ottersen, G. (2003). Forecasting recruitment and stock biomass of northeast Arctic cod using neural networks. Sci. Mar. (Barc.) 2003: 325-335
In: Scientia Marina (Barcelona). Consejo Superior de Investigaciones Científicas. Institut de Ciènces del Mar: Barcelona. ISSN 0214-8358, more
Peer reviewed article  

Also published as
  • Huse, G.; Ottersen, G. (2003). Forecasting recruitment and stock biomass of northeast Arctic cod using neural networks, in: Ulltang, Ø. et al. Fish stock assessments and predictions: integrating relevant knowledge: SAP Symposium held in Bergen, Norway 4-6 December 2000. Scientia Marina (Barcelona), 67(Suppl. 1): pp. 325-335, more

Available in  Authors 
Document type: Conference paper

Keywords
    Abundance; Algorithms; Gadoid fisheries; Genetics; Growth; Population dynamics; Prediction; Recruitment; Temperature; Clupea harengus Linnaeus, 1758 [WoRMS]; Gadus morhua Linnaeus, 1758 [WoRMS]; Mallotus villosus (Müller, 1776) [WoRMS]; Marine

Authors  Top 
  • Huse, G., more
  • Ottersen, G.

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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