Open WL Archief (OWA)

Over het archief

Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.

Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.

Basisinformatie over ‘Open Access to scholarly information'.

[ meld een fout in dit record ]

mandje (1): toevoegen | toon

---

one publication added to basket [36444]

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.) 67(S1): 325-335. https://dx.doi.org/10.3989/scimar.2003.67s1325 In: Scientia Marina (Barcelona). Consejo Superior de Investigaciones Científicas. Institut de Ciènces del Mar: Barcelona. ISSN 0214-8358; e-ISSN 1886-8134, meer Ook verschenen in: Ulltang, Ø.; Blom, G. (2003). Fish stock assessments and predictions: integrating relevant knowledge. SAP Symposium held in Bergen, Norway 4-6 December 2000. Scientia Marina (Barcelona), 67(S1). Institut de Ciències de Mar: Barcelona. 374 pp. https://dx.doi.org/10.3989/scimar.2003.67s1, meer

---

Beschikbaar in	Auteurs
VLIZ: Open access 67488 [ download pdf ]

---

Trefwoorden

Abundance Algorithms Biology > Genetics Fisheries > Finfish fisheries > Gadoid fisheries Population dynamics Population functions > Growth Population functions > Recruitment Prediction Properties > Physical properties > Thermodynamic properties > Temperature Clupea harengus Linnaeus, 1758 [WoRMS]; Gadus morhua Linnaeus, 1758 [WoRMS]; Mallotus villosus (Müller, 1776) [WoRMS] Marien/Kust

---

Auteurs		Top
Huse, G., meer 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.

---

Alle informatie in het Integrated Marine Information System (IMIS) valt onder het VLIZ Privacy beleid

Top | Auteurs

IMIS is ontwikkeld en wordt gehost door het VLIZ.