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Irrigation water quality evaluation using adaptive network-based fuzzy inference system

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Abstract

Water quality diagrams are comprised of quality classes defined by crisp sets, and as a consequence the boundaries between classes have an inherent imprecision. In this study, the concentration values of electrical conductivity (EC) and sodium adsorption ratio (SAR) in United States Salinity Laboratory diagram (USSL) are combined together through an adaptive network-based fuzzy inference system (ANFIS) to generate a new method that can be used instead of the USSL-diagram. The results showed that water quality classification based on the proposed method is more precise in comparison with the USSL-diagram classification, and it is a promising alternative to traditional approach. It has been observed that the ANFIS model with 96% accuracy has much better predicting capability than the Mamdani fuzzy inference system (MFIS). The results indicated that the ANFIS modeling decreases error effects in hydro-chemical experiments and it also significantly decreases computation time for the irrigation water quality evaluation.

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References

  • Abraham A (2005) Rule-based expert systems. In: Sydenham PH, Thorn R (eds) Handbook of measuring system design. Wiley, New York. ISBN: 0-470-02143-8

  • Herrera F, Lozano M (2003) Fuzzy adaptive genetic algorithm: design, taxonomy, and future directions. Soft Comput 7:545–562

    Google Scholar 

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

    Article  Google Scholar 

  • Jacquin AP, Shamseldin AY (2006) Development of rainfall–runoff models using Takagi–Sugeno fuzzy inference systems. J Hydrol 329:154–173

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cyber 23(3):665–685

    Article  Google Scholar 

  • Jang JSR, Sun C, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Englewood Cliffs, NJ, USA

    Google Scholar 

  • Mamdani E, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13

    Article  Google Scholar 

  • MathWorks (2004) Fuzzy logic toolbox user’s guide, for use of the matlab. The Math Works Inc. http://www.mathworks.com/

  • Mazloumzadeh SM, Shamsi M, Nezamabadi-pour H (2008) Evaluation of general-purpose lifters for the date harvest industry based on a fuzzy inference system. Comput Electron Agric 60(1):60–66. doi:10.1016/j.compag.2007.06.005

    Article  Google Scholar 

  • Mazloumzadeh SM, Shamsi M, Nezamabadi-pour H (2009) Fuzzy logic to classify date palm trees based on some physical properties related to precision agriculture. Precis Agric (in press). doi:10.1007/s11119-009-9132-2

  • Mirabbasi R, Mazloumzadeh SM, Rahnama MB (2008) Evaluation of irrigation water quality using fuzzy logic. Res J Environ Sci 2:340–352. doi:10.3923/rjes.2008.340.352

    Article  CAS  Google Scholar 

  • Muhammetoglu A, Yardimci A (2006) A fuzzy logic approach to assess groundwater pollution levels below agricultural fields. J Environ Monit Assess 118:337–354

    Article  CAS  Google Scholar 

  • Nasiri F, Maqsood I, Huang G, Fuller N (2007) Water quality index: a fuzzy river-pollution decision support expert system. J Water Resour Plan Manag 133(2):95–105

    Article  Google Scholar 

  • Ocampo-Duque W, Ferré-Huguet N, Domingo JL, Schuhmacher M (2006) Assessing water quality in rivers with fuzzy inference systems: a case study. Environ Int 32:733–742

    Article  CAS  PubMed  Google Scholar 

  • Schoeller H (1962) Les eaux sutter raines. Masson et cie. 67, Paris

  • Silvert W (2000) Fuzzy indices of environmental conditions. Ecol Model 130:111–119

    Article  CAS  Google Scholar 

  • Stiff HA Jr (1951) The interpretation of chemical water analysis by means of patterns. J Petrol Technol 3:15–16

    Google Scholar 

  • Sugeno M, Kang GT (1988) Structure identification of fuzzy models. Fuzzy Sets Syst 28:15

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. Trans Syst Man Cyber 15(1):116–132

    Google Scholar 

  • U.S. Public Health Service (1962) Public health service drinking water standards: U.S. Dept. H.E.W., Public Health Service, Publ. 956

  • U.S. Salinity Laboratory Staff (1954) Diagnosis and improvement of saline and alkali soils: U.S. Dept. Agric. Handbook No. 60, 160 p

  • Wilcox LV (1955) Classification and use of irrigation waters: U.S. Dept. Agric. Circ. 969, 19 p

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

Download references

Acknowledgment

This research is supported in part by the Fuzzy Systems and Applications Center of Exellence, Shahid Bahonar University of Kerman, Kerman, Iran.

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Authors and Affiliations

  1. Department of Agricultural Machinery, Shahid Bahonar University of Kerman, Kerman, Iran

    N. Alavi

  2. Department of Mechanic, Islamic Azad University, Izeh Branch, Khoozestan, Iran

    V. Nozari

  3. Young Researchers Club, Islamic Azad University, Ghayenat Branch, Southern Khorasan, Iran

    S. M. Mazloumzadeh

  4. Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    H. Nezamabadi-pour

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  1. N. Alavi
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  2. V. Nozari
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  3. S. M. Mazloumzadeh
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  4. H. Nezamabadi-pour
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Correspondence to S. M. Mazloumzadeh.

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Alavi, N., Nozari, V., Mazloumzadeh, S.M. et al. Irrigation water quality evaluation using adaptive network-based fuzzy inference system. Paddy Water Environ 8, 259–266 (2010). https://doi.org/10.1007/s10333-010-0206-6

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  • DOI: https://doi.org/10.1007/s10333-010-0206-6

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