Fuzzy Logic: adding natural uncertainties into environmental assessment

Abstract

This research study sought to evaluate aimed at evaluating the possible advantages of using Fuzzy Logic as opposed to Boolean Logic to assess environmental contamination and source appraisal for polycyclic aromatic hydrocarbons (PAH). Results obtained through traditional assessment tools for two different tropical coastal areas through using traditional clustering and principal components analysis were compared with those derived from the Fuzzy Logic, using the by Fuzzy C-means algorithm. The feedings achieved through Fuzzy Logic showed a greater qualitative detail than those derived from traditional tools. The abrupt and unnatural changes obtained from the usual classification methods were avoided by having membership values varying continuously in space, providing a more accurate picture of environmental contamination in complex and multiple sources environments. Furthermore, by not depending on statistic suppositions distribution of data like other methods, becomes more suitable for environmental data. Although Fuzzy Logic does not produce quantitative interpretations, its application generates adequate the data needed to avoid environmental management bias in the inference of contamination sources.