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Can machine learning techniques help to improve the common fisheries policy?

Authors

PÉREZ ORTIZ, MARÍA, COLMENAREJO FERNÁNDEZ, ROSA, Fernández Caballero J.C. , Hervás-Martínez C.

External publication

No

Means

Lect. Notes Comput. Sci.

Scope

Conference Paper

Nature

Científica

JCR Quartile

SJR Quartile

SJR Impact

0.36

Publication date

01/01/2013

Scopus Id

2-s2.0-84880063904

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. © 2013 Springer-Verlag Berlin Heidelberg.

Keywords

Environmental impact study; Fisheries policy; Fishing fleets; Fishing gears; Machine learning techniques; Ordinal classification; Over capacity; Overexploitation; Fish; Learning systems; Neural networks; Sustainable development; Fisheries

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