Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Can Machine Learning Techniques Help to Improve the Common Fisheries Policy?
verfasst von : María Pérez-Ortiz, Rosa Colmenarejo, Juan Carlos Fernández Caballero, César Hervás-Martínez
Erschienen in: Advances in Computational Intelligence
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The overcapacity of the European fishing fleets is one of the recognized factors for the lack of success of the Common Fisheries Policy. Unwanted non-targeted species and other incidental fish likely represent one of the causes for the overexploitation of fish stocks; thus there is a clear connection between this problem and the type of fishing gear used by vessels. This paper performs an environmental impact study of the Spanish Fishing Fleet by means of ordinal classification techniques to emphasize the need to design an effective and differentiated common fish policy for “artisan fleets”, that guarantees the maintenance of environmental stocks and the artesan fishing culture.