Can machine learning techniques help to improve the Common Fisheries Policy?

Áreas de investigación:

• Methodology - Machine learning

Año:

2013

Tipo de publicación:

Artículo en conferencia

Palabras clave:

Machine learning, Ordinal Classification, Commitment to sustainability, Common Fisheries Policy, Fleet Overcapacity

Autores:

• Pérez-Ortiz, María
• Colmenarejo, R.
• Fernández, Juan Carlos
• Hervás-Martínez, César

Volumen:

7903

Título del libro:

International Work Conference on Artificial Neural Networks

Serie:

Advances in Computational Intelligence, Part II, Lecture Notes in Computer Science

Páginas:

278-286

Organización:

Puerto de la Cruz, Spain

Mes:

12-14 June

Abstract:

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.