Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
AuthorsFernandes, José Antonio
Lozano, Jose A.
KAUST DepartmentRed Sea Research Center (RSRC)
Biological and Environmental Sciences and Engineering (BESE) Division
Marine Science Program
Plankton ecology Research Group
Permanent link to this recordhttp://hdl.handle.net/10754/563986
MetadataShow full item record
AbstractThe effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.
CitationFernandes, J. A., Irigoien, X., Lozano, J. A., Inza, I., Goikoetxea, N., & Pérez, A. (2015). Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. Ecological Informatics, 25, 35–42. doi:10.1016/j.ecoinf.2014.11.004
SponsorsThe research of Jose A. Fernandes and Nerea Goikoetxea is supported by a Doctoral Fellowship from the Fundacion Centros Tecnologicos Inaki Goenaga. This study has been supported by the following projects: Ecoanchoa (funded by the Department of Agriculture, Fisheries and Food of the Basque Country Government); the Saiotek and Research Groups 2007-2012 (IT-242-07) programs (Basque Government), TIN2008-06815-C02-01 (Spanish Ministry of Education and Science); COMBIOMED network in computational biomedicine (Carlos III Health Institute); the EU project UNCOVER; the EU FACT; and the EU VII Framework project MEECE (MEECE No 212085). Professor Michael Collins (SOES, University of Southampton, UK and AZTI-Tecnalia, Spain) is acknowledged for his comments on the manuscript and help with the English language. This is contribution 695 from the Marine Research Division (AZTI-Tecnalia).