Can Fuzzy Logic Bring Complex Environmental Problems into Focus?

In everyday life and field such as environmental health /environmental impacts people deal with concepts that involve factors that defy classification into crisp sets safe/minimal, harmful/ very high negative impacts, acceptable with mitigation measures, and so on. A classic example is a regulator carefully explaining the result of a detailed quantitative risk assessment/environmental impact assessment report to a community group, only to be asked over and over again. But are we safe? / But are environmental impacts minimal? In this case, safe/minimal defies crisp classification because it is a multivariate state with gradation that varies among different individuals and groups. Furthermore, it is hard to define the terms like health, environment, and hazardous, safe, air and water quality, risk and alike as these are vague or fuzzy terms based on perception. In July 1964, Professor Lotfi Zadeh, while working on the problems in pattern classification and system analysis thought of the use of imprecise categories for classification, and the idea of grade of membership, which is the concept that became the backbone of fuzzy set theory, occurred to him then. This important event led to the publication of his seminal paper: Fuzzy Sets (1965) and the birth of fuzzy logic technology. In this sequel, we consider how fuzzy logic applies to two important issues of environment management systems: 1] river water quality classification and 2] ranking of industries based on their hazardous pollution potential. The presentation is primarily centered on fuzzy sets and fuzzy rule based systems, aimed at straightly defining one of the components of environmental quality straightway in linguistic terms with degree of certainty. Would decision makers and the public accept expressions environmental quality goals in straightway linguistic terms with computed degrees of certainty? Resistance is likely. In many regions, such as the United States and European Union, both decision makers and members of the public seem more comfortable with the current system—in which government agencies avoid confronting uncertainties by setting guidelines that are crisp and often fail to communicate uncertainty. Perhaps someday a more comprehensive approach that includes exposure surveys, toxicological data, and epidemiological studies coupled with fuzzy modeling (could be termed as hybrid fuzzy- Probability modeling) will go a long way toward resolving some of the conflict, divisiveness, and controversy in the current regulatory paradigm.