Fuzzy model to assess the water quality in the Meia Ponte river watershed under different standpoints

• Vinícius Sebba Patto Université Pierre et Marie Curie
• Gelson da Cruz Junior UFG
• Luis Maurício Bini UFG
• Marco Antonio Assfalk De Oliveira UFG

Resumo

This paper addresses a fuzzy model to assess the quality of the water through the use of three different indices. To compose these indices, we considered the use of the water and the characteristics of the Meia Ponte river watershed. At first, we will present the context, the assessment of water quality considering the complexity of the water, its parameters, and its application, as well as the non-significant relationship between parameters employed to assess its quality. Our main goal is to help the assessment of water quality, avoiding the influence of parameters on each other and taking into account the destination of the water. Then, we will present the model and the fuzzy inference process. At the end, a case study is presented in order to help to compare the results of the three proposed indices with the results obtained from the classical model proposed by the National Sanitary Foundation of the United States to determine the index of the water quality. To finish, we discuss it.

Referências

CETESB (2001). Relatório de qualidade das águas interiores do estado de São Paulo. Technical report, Companhia de Tecnologia de Saneamento Ambiental.

CETESB (2004). Relatório de qualidade das águas interiores do estado de São Paulo. Technical report, Companhia de Tecnologia de Saneamento Ambiental.

Chang, F. J., C. Y. T. (2006). Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. volume 9, pages 1–10. Elsevier.

Cox, E. (1994). The Fuzzy Systems Handbook: A Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems. Elsevier Science & Technology Books, 1rst edition.

Derísio, J. C. (2000). Introdução ao controle de poluição ambiental. Signus Editora, 2nd edition.

Horton, R. K. (1965). An index-number system for rating water quality. pages 300–306. Water Pollution Control Fed.

Jang, J.-S. R., S. C.-T. M. E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach To Learning And Machine Intelligence. Prentice-Hall.

Karamouz, M., J. N. K. R. (2004). River water quality zoning: a case study of karoon and dez river system. volume 1, pages 16–27. Iranian Journal of Environmental Health Science and Engineering.

Lu, R.-S., L. S.-L. (2002). Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. Water Research, 36:2265–2274.

Nives, S.-G. (2003). Comparison of dalmation water evaluation indices. volume 75, pages 388–405. Water Environment Research.

Ross, T. J. (1997). Fuzzy Logic with Engineering Applications. McGraw-Hill, Inc., 1st edition.

Said, A., S. D. K.-S.-G. (2004). An innovative index for evaluating water quality in streams. volume 34, pages 406–414. Environmental Management.

Soyupak, S., C. D.-G. (2004). Fuzzy logic model to estimate seasonal pseudo steady state chlorophyll-a concentrations in reservoirs. volume 9, pages 51–59. Springer.