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

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dc.contributor.authorYildiz, Sayiter
dc.contributor.authorKarakus, Can Bulent
dc.date.accessioned2024-10-26T18:02:11Z
dc.date.available2024-10-26T18:02:11Z
dc.date.issued2020
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractSurface 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 (R-2) 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 (mu), standard error (SE), standard deviation (sigma), R-2, root mean square error (RMSE) and mean absolute percentage error (MAPE). The training R-2 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 (R-2 = 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.
dc.identifier.doi10.1007/s10668-019-00405-5
dc.identifier.endpage4786
dc.identifier.issn1387-585X
dc.identifier.issn1573-2975
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85072131031
dc.identifier.scopusqualityQ1
dc.identifier.startpage4771
dc.identifier.urihttps://doi.org/10.1007/s10668-019-00405-5
dc.identifier.urihttps://hdl.handle.net/20.500.12418/27989
dc.identifier.volume22
dc.identifier.wosWOS:000528625000043
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironment Development and Sustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial neural network
dc.subjectIrrigation water quality index
dc.subjectMultiple regression
dc.titleEstimation of irrigation water quality index with development of an optimum model: a case study
dc.typeArticle

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