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Can Fuzzy Logic Via Computing with Words Bring Complex Environmental Issues into Focus?

  • Ashok DeshpandeEmail author
  • Jyoti Yadav
Conference paper

Abstract

Information on the status and changing trends in environmental quality is necessary to formulate sound public policy and efficient implementation of environmental pollution abatement programmes. In this quest, water/air quality indices are computed using US-EPA and US-NSF proposed methods for local and regional water/air quality management in many metro cities of the world. There are different types of uncertainties while adopting the procedure in vogue in the computation of these indices. However, it does not include expert’s knowledge with a view to arrive at cause–effect relationship. We believe that the development of a method to quantify association between the pollutant and air/water-borne diseases is an important step before classifying air/water quality, either in numeric or linguistic terms. There exists aleatory uncertainty in the pollution parametric data and epistemic uncertainty in describing the pollutants by the domain experts in linguistic terms such as poor, good, and very good. Successes of probability theory have high visibility. But what is not widely recognised is that these successes mask a fundamental limitation—the inability to operate on what may be called perception-based information. In this chapter, we describe the case study 1 that relates to fuzzy description of river water quality in River Ganga for bathing purpose, while case study 2 presents fuzzy description of air quality in Pune City.

Keywords

Bathing River water quality Fuzzy set theory Linguistic terms Fuzzy number Degree of match Fuzzy rule base system Degree of certainty 

Notes

Acknowledgement

The wholehearted assistance received from Dr. D. V. Raje and Dr. Kedar Rijal for the implementation of the concept is gratefully acknowledged. My special thanks to Professor Thomas McKone who helped the author in many fuzzy ways!

References

  1. 1.
    Deshpande AW, Raje DV, Khanna P (1996). Fuzzy description of river water quality. Eufit 96, 2–5 September. pp 1795–1801Google Scholar
  2. 2.
    Rijal K, Deshpande A, Ghole V (2009) Bathing in polluted rivers, water-borne diseases, and fuzzy measures: a case study in India. Int. J. Environ Waste Manage 6(3–4):255–263Google Scholar
  3. 3.
    Mckone TE, Deshpande AW (2005) Can fuzzy logic bring complex environmental problems into focus? Int J Environ Sci Technol 39(2):42A–47ACrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Berkeley Initiative in Soft Computing (BISC)-Special Interest Group (SIG)-Environment Management Systems (EMS)University of CaliforniaBerkeleyUSA
  2. 2.College of Engineering Pune (COEP)PuneIndia
  3. 3.National Environmental Engineering Research Institute (NEERI)NagpurIndia
  4. 4.Department of Computer ScienceUniversity of PunePuneIndia

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