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

Sayiter Yıldız () and Can Bülent Karakuş ()
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Sayiter Yıldız: Sivas Cumhuriyet University
Can Bülent Karakuş: Sivas Cumhuriyet University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2020, vol. 22, issue 5, No 43, 4786 pages

Abstract: 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

Keywords: Artificial neural network; Irrigation water quality index; Multiple regression (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10668-019-00405-5

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