A fuzzy logic inference system to assess fishery productivity in coastal fishing grounds

Abstract

Fuzzy logic provides a powerful tool to capture the uncertainties associated with human cognitive processes. In the present study, a methodology based on Mamdani-type fuzzy inference system (FIS) was applied to classify the Greek coastal fishing landings according to their fishery productivity during the period 1988-2005. However, five fuzzy sets to split the inputs and outputs have been considered suitable for the scope for this study. Eight common fish species have been selected to be the indicators for the classification of the fishing grounds and two hundred eighty nine inference rules (expressed in IF-THEN clauses) were developed. This fuzzy inference system has advantages in flexibility of input data, in the explicit representation of uncertainty and in the ease of incorporating new knowledge, so it can be used as a decision support tool in fishery management.