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Fuzzy process capability indices for quality control of irrigation water

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Abstract

Water covers over 70% of the Earth surface and is a very important resource to people and the environment. Water pollution affects drinking water, rivers, lakes and oceans all over the world. This consequently harms human health and the natural environment. Water pollution can also affect the crops. So, water pollution is an important issue for humanity. Therefore, the control of irrigation water is a necessity. In this paper, a methodology based on process capability indices (PCIs) has been presented to control the levels of pH, dissolved oxygen (DO) and temperature (T) in dam’s water for irrigation. Fuzzy PCIs have been proposed for this aim. The fuzzy estimates of \( \hat C_p \) and \( \hat C_{pk} \) are obtained for pH, DO, and T based on Buckley’s interval estimation approach and based on fuzzy specification limits. An application has been made for Kesikköprü Dam in Ankara, Turkey. In this paper, Buckley’s approach is re-arranged to obtain a triangular fuzzy membership function because it cannot be obtained from Buckley’s approach in some situation.

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References

  • Anonymous (1991) Water pollution control, The Official Gazette, whose number is 20748, Ankara

  • Anonymous (1998) Water pollution control regulations, no. 19919, 13–74 (in Turkish)

  • Anonymous (2003) Environment protection (Water Quality) policy 2003 and explanatory report. http://www.epa.sa.gov.au/pdfs/epwq_report.pdf

  • Anonymous (2006) Land and water resources of Turkey, Reports of DSI˙ (The General Directorate of State Hydraulic Works). http://www.dsi.gov.tr/english/topraksue.htm

  • Bothe DR (1997) Measuring process capability: techniques and calculations for quality and manufacturing engineers. McGraw-Hill, New York

  • Boyles RA (1991) The Taguchi capability index. J Qual Technol 23:17–26

    Google Scholar 

  • Buckley JJ (2004) Fuzzy statistics, studies in fuzziness and soft computing. Springer Book, New York

  • Buckley JJ (2005a) Fuzzy statistics: regression and prediction. Soft Comput 9:769–775

    Article  Google Scholar 

  • Buckley JJ (2005b) Fuzzy statistics: hypothesis testing. Soft Comput 9:512–518

    Article  Google Scholar 

  • Chen TW, Chen KS, Lin JY (2003a) Fuzzy evaluation of process capability for bigger-the-best type products. Int J Adv Manuf Technol 21:820–826

    Article  Google Scholar 

  • Chen TW, Lin JY, Chen KS (2003b) Selecting a supplier by fuzzy evaluation of capability indices Cpm. Int J Adv Manuf Technol 22:534–540

    Article  Google Scholar 

  • Chen KS, Chen TW (2007) Multi-process capability plot and fuzzy inference evaluation. Int J Prod Econ. doi:10.1016/j.ijpe.2006.12.056

  • Gao Y, Huang M (2003) Optimal process tolerance balancing based on process capabilities. Int J Adv Manuf Technol 21:501–507

    Article  Google Scholar 

  • Hsu BM, Shu MH (2007) Fuzzy inference to assess manufacturing process capability with imprecise data. doi:10.1016/j.ejor.2007.02.023

  • Icaga Y, Bostanoglu Y, Kahraman E (2006) Water quality statistics of Akarçay Basin. Technol Res EJCT 2(1):43–50

    Google Scholar 

  • Icaga Y (2007) Fuzzy evaluation of water quality classification. Ecol Ind 7(3):710–718

    Article  Google Scholar 

  • Kane VE (1986) Process capability indices. J Qual Technol 18(1):41–52

    Google Scholar 

  • Kotz S, Johnson N (2002) Process capability indices-a review 1992–2000. J Qual Technol 34:2–19

    Google Scholar 

  • Lee YH, Wei CC, Chang CL (1999) Fuzzy design of process tolerances to maximise process capability. Int J Adv Manuf Technol 15:655–659

    Article  Google Scholar 

  • Lee HT (2001) Cpk index estimation using fuzzy numbers. Eur J Oper Res 129:683–688

    Article  Google Scholar 

  • Montgomery DC (2005) Introduction to statistical quality control. Wiley, New York

    Google Scholar 

  • Parchami A, Mashinchi M, Yavari AR, Maleki HR (2005) Process capability indices as fuzzy numbers. Aust J Stat 34(4):391–402

    Google Scholar 

  • Parchami A, Mashinchi M, Maleki HR (2006) Fuzzy confidence interval for fuzzy process capability index. J Intell Fuzzy Syst 17:287–295

    Google Scholar 

  • Parchami A, Mashinchi M (2007) Fuzzy estimation for process capability indices. Inf Sci 177:1452–1462

    Article  Google Scholar 

  • Tsai CC, Chen CC (2006) Making decision to evaluate process capability index C p with fuzzy numbers. Int J Adv Manuf Technol 30:334–339

    Article  Google Scholar 

  • Yongting C (1996) Fuzzy quality and analysis on fuzzy probability. Fuzzy Sets Syst 83:283–290

    Article  Google Scholar 

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Correspondence to İhsan Kaya.

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Kahraman, C., Kaya, İ. Fuzzy process capability indices for quality control of irrigation water. Stoch Environ Res Risk Assess 23, 451–462 (2009). https://doi.org/10.1007/s00477-008-0232-8

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  • DOI: https://doi.org/10.1007/s00477-008-0232-8

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