Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

• Published in: Ecological Informatics
• Main Authors: Fernandes, JA, Irigoien, X, Lozano, JA, Inza, I, Goikoetxea, N, Pérez, A
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2015
• Subjects: Biology; Computer Science; Data and Information; Ecology and Environment; Fisheries; Management; Marine Sciences; Meteorology and Climatology; Oceanography; Planning; Policies; Technology; Hake; North East Atlantic
• Online Access: http://plymsea.ac.uk/id/eprint/6863/; https://doi.org/10.1016/j.ecoinf.2014.11.004

The 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...