A review of fuzzy set theoretic approaches and their applications in environmental practice

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

Fuzzy set theory deals with characterizing and quantifying vague and imprecise information and as such can be of high value in environmental practice. While statistical randomness arises due to lack of adequate data and variability in a system, fuzziness incorporates imprecision arising from lack of complete information, conflicting viewpoints or theories, incomplete or incorrect models of physical phenomenon and subjective decisions made during analysis. Clearly, fuzziness is quite prevalent in environmental practice and must be quantified for risk-informed decision making. While fuzzy logic approaches offer several advantages, lack of understanding and training among practitioners is seen as a significant bottleneck in routine utilization of fuzzy based schemes. Conventional mathematical approaches, such as establishing relationships between inputs and outputs, regression, calculus, and hypothesis testing can all be carried out on fuzzy variables. The literature on fuzzy set-theoretic approaches is rich and a significant amount of research is being carried out to identify solutions to new problems and refine existing approaches. Several theoretical contributions suitable for use in environmental practice have been identified in this review. In addition, selected example environmental applications where fuzzy logic based schemes have been employed have also been documented. It is hoped that environmental practitioners and regulators will appreciate the utility of fuzzy logic in characterizing imprecision that plagues environmental analysis and utilize its functionality in routine risk, remedial, regulatory and other applications.

Original languageEnglish
Title of host publicationContaminated Soils, Sediments and Water
PublisherSpringer US
Pages501-515
Number of pages15
Volume9
ISBN (Print)038723036X, 9780387230368
DOIs
StatePublished - 2005

Keywords

  • Decision making
  • Imprecision
  • Mathemathical models
  • RBCA
  • Vagueness

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