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

  • Sayiter Yıldız
  • Can Bülent KarakuşEmail author


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 < 0.05) was detected. As the calculated F value was higher than the tabulated F value, a real relationship between the dependent and independent variables is inferred. Four different models were built with ANN, and the statistical performance of the models was determined using statistical parameters such as average value (µ), standard error (SE), standard deviation (σ), R2, root mean square error (RMSE) and mean absolute percentage error (MAPE). The training R2 value belonging to the best model was found to be significantly high (0.99). The relation between the estimation results of ANN model and the experimental data (R2 = 0.92) verifies the model’s success. As a result, ANN proved to be a successful means for IWQI estimation using different water quality parameters.


Artificial neural network Irrigation water quality index Multiple regression 



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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Environmental Department, Engineering FacultySivas Cumhuriyet UniversitySivasTurkey
  2. 2.Urban and Regional Planning Department, Architecture FacultySivas Cumhuriyet UniversitySivasTurkey

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