Title 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 Lecture Notes in Computer Science
Scope Conference Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.36
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880063904&doi=10.1007%2f978-3-642-38682-4_31&partnerID=40&md5=75a328fbeebeaa21cd4a9dc1279ad5b4
Publication date 01/01/2013
Scopus Id 2-s2.0-84880063904
DOI 10.1007/978-3-642-38682-4_31
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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