Fuzzy indices of environmental conditions

Abstract

Fuzzy logic provides a powerful and convenient formalism for classifying environmental conditions and for describing both natural and anthropogenic changes. Whereas traditional indices are based either on crisp sets with discontinuous boundaries between them (e.g. pristine vs. polluted), or on continuous variables whose values are only meaningful to experts (such as so many ppm of a toxin), fuzzy sets make it possible to combine these approaches. Conceptually the use of fuzzy logic is simple (for example, one can describe a site as 20% pristine and 80% polluted), but the real power of the methodology comes from the ability to integrate different kinds of observations in a way that permits a good balance between favourable and unfavourable observations, and between incommensurable effects such as social, economic, and biological impacts. In addition, fuzzy logic can be used to classify and quantify environmental effects of a subjective nature, such as bad odours, and it even provides a formalism for dealing with missing data. The fuzzy memberships can be used as environmental indices, but it is also possibly to ‘defuzzify’ them and obtain a more traditional type of index. The fuzzy methodology is illustrated by examples based on research to evaluate of the effects of finfish mariculture on coastal zone water quality.