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Estimation of irrigation water quality index with development of an optimum model: a case study

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  • Sayiter Yıldız

    (Sivas Cumhuriyet University)

  • Can Bülent Karakuş

    (Sivas Cumhuriyet University)

Abstract

Surface water quality parameters are important means for determination of water’s suitability for irrigation. In this research, data from 32 irrigation stations were used to calculate the sodium adsorption rate (SAR), sodium percentage (Na%), Kelly index (KI), permeability index (PI) and irrigation water quality index (IWQI) for evaluation of surface water quality. The obtained SAR, KI and Na% values, respectively, varied between 0.10 and 9.43, 0.03–1.37 meq/l and 3.16–57.82%. The calculated PI values indicate that, 93.75% of the water samples is in “suitable” category, and 6.25% is in “non-suitable” category. The IWQI values obtained from the research area varied between 30.59 and 81.09. In terms of irrigation water quality, 12.5% of the samples is of “good” quality, 15.62% is of “poor” quality, 68.75% is of “very poor” quality, and 3.12% is of “non-suitable” quality. Accordingly, IWQI value was estimated on the basis of SAR, Na%, KI and PI values using multiple regression and artificial neural network (ANN) model. The regression coefficient (R2) was determined as 0.6 in multiple regression analysis, and a moderately significant relationship (p

Suggested Citation

  • Sayiter Yıldız & Can Bülent Karakuş, 2020. "Estimation of irrigation water quality index with development of an optimum model: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4771-4786, June.
    • Handle: RePEc:spr:endesu:v:22:y:2020:i:5:d:10.1007_s10668-019-00405-5
    DOI: 10.1007/s10668-019-00405-5
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    2. Antoine Bouët & Yvan Decreux & Lionel Fontagné & Sébastien Jean & David Laborde, 2008. "Assessing Applied Protection across the World," Review of International Economics, Wiley Blackwell, vol. 16(5), pages 850-863, November.
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    Cited by:

    1. Docheshmeh Gorgij, A. & Askari, Gh & Taghipour, A.A. & Jami, M. & Mirfardi, M., 2023. "Spatiotemporal Forecasting of the Groundwater Quality for Irrigation Purposes, Using Deep Learning Method: Long Short-Term Memory (LSTM)," Agricultural Water Management, Elsevier, vol. 277(C).

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