Ranking and classification of fishing areas using fuzzy models and techniques

Abstract

P>Fuzzy-logic-based methods and fuzzy logic formalism have been demonstrated as appropriate to address the uncertainty and subjectivity in complex environmental problems. This study investigates the use of three fuzzy logic methods in fisheries analysis, aiming towards the grouping and ranking of fishing subareas, according to their fisheries yield. Initially, a simple fuzzy c-means clustering model was applied to the fishing subareas examined. A rule-based Mamdani-type fuzzy inference system was then developed to allow the direct fishing subarea classification. Finally, a species-economic value weighted global fuzzy membership model was introduced, serving as an indirect classification and ranking scheme. Global memberships were plotted on simple ternary diagrams, producing representations that serve as tools in fisheries management. All methods examined the performance of the Greek fishing subareas, based on the annual landings series of the 10 most abundant fish species in terms of landed biomass, during the period 1985-1999.