Fuzzy multiple criteria decision making approach in environmental risk assessment

Andrejs Radionovs, Oleg Uzhga-Rebrov


Being able to evaluate risks is an important task in many areas of human activity: economics, ecology, etc. In case of a sufficient amount of source information the risk is evaluated using statistical methods. However, in reality the sufficiency of statistical data in risk assessment is more exceptional than normal. In such cases experts’ assessment make the only source of data. Experts are able to provide the necessary for analysis data due to their professional knowledge and experience. Certain amount of factors, which is to be evaluated by an expert (experts), significantly affects the process of experts’ assessment. If a big number of relevant factors occur, an expert may face a problem of defining links between “factors” and “outcome”. Fuzzy multiple criteria decision making approach can be used to solve the problem. Ecological risk assessment towards human health in case of gaseous substances escape at a chemical factory using hierarchical method and fuzzy multiple criteria decision making approach has been analyzed in the article.


fuzzy logic; risk assessment; fuzzy representation of knowledge

Full Text:



Carlon, C., Critto, A., Marcomini, A., Nathanail, P. “Risk based characterisation of contaminated industrial site using multivariate and geostatistical tools”, Environmental Pollution, vol. 111 (3), 2001, pp. 417-427.

Burningham, K., Thrush, D. “Pollution concerns in context: A comparison of local perceptions of the risks associated with living close to a road and a chemical factory”, Journal of Risk Research, vol. 7 (2), 2004, pp. 213-232.

Martin B., Pearson A., Bauer B. “An Ecological Risk Assessment of Wind Energy”, The Nature Conservancy Helena, Montana, pp.146, 2009

L. Zadeh, “Fuzzy Sets” Information Control, 1965, 8, pp. 338 – 353.

M.R. Akbarzadeh-T, M. Moshtagh-Khorasani “A hierarchical fuzzy rule-based approach to aphasia diagnosis.”, Journal of Biomedical Informatics, Vol. 40, No. 5, 2007, pp. 465–475

M. Ganesh, Introduction to Fuzzy Sets and Fuzzy Logic. PHI Learning Pvt. Ltd, 2006, 256 p.

G.J. Klir, Y. Bo, Fuzzy Sets and Fuzzy Logic. Prentice Hall, 1995, 592 p.

T.E. McKone, A.W. Deshpande,, 2005. Environ. Sci. Technol. 39 (2) 42A.

L.A. Zadeh, 1983. The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems, Fuzzy Sets and Systems 11, 199.

L. Zadeh, “Fuzzy logic and approximate reasoning” Synthese, Vol 30, 1975, pp. 407 – 428.

Bellman, R., Adaptive Control Processes, Princeton University Press, Princeton, 1966.

Cordón O., Herrera, F., Peregrín A., “Looking for the Best Defuzzification Method Features for each Implication Operator to Design Accurate Fuzzy Models” ,Technical Report, DECSAI, 1999.

Garibaldi, J. M. and Ifeachor, E.C., “ Application of simulated Annealing Fuzzy Model Tuning to UmbilicalCord Acid-base Interpretation ”, IEEE Transactions on Fuzzy Systems , Vol.7, No.1. 1999.

R. E. Bellman, L. A. Zadeh, "Decision making in a fuzzy environment,"Management Science, vol. 17, no. 4, 1970, pp. B141-B164

Mamdani, E.H. and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, Vol. 7, No. 1, 1975, pp. 1-13.

Klir, G. J., and Yuan, B., Fuzzy Sets, Fuzzy Logic and Fuzzy System, World Scientific Singapore, 1996.

Ross, T. J., "Properties of membership functions, fuzzification, and defuzzification" in Fuzzy Logic with Engineering Applications, Third Edition, John Wiley & Sons, Ltd, Chichester, United Kingdom, 2010, pp. 89–116.

DOI: http://dx.doi.org/10.17770/etr2015vol3.169


  • There are currently no refbacks.

SCImago Journal & Country Rank