Document of bibliographic reference 36444

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

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

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.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000183403500026

Bibliographic citation

Huse, G.; Ottersen, G. (2003). Forecasting recruitment and stock biomass of northeast Arctic cod using neural networks. Sci. Mar. (Barc.) 67(S1): 325-335. https://dx.doi.org/10.3989/scimar.2003.67s1325

Topic

Marine

Is peer reviewed

true

Access rights

open access

Is accessible for free

true