Article: Robust computational modelling of the sodium adsorption ratio using regression analysis and support vector machine Journal: International Journal of Data Science (IJDS) 2020 Vol.5 No.3 pp.203 - 228 Abstract: In present study, two new methods including least-square support vector machine (LSSVM) and regression-based model, were created for accurate estimation of the adsorption ratio of sodium in terms of ionic concentrations of calcium (Ca2+), magnesium (Mg2+), and sodium (Na+); the bicarbonate (HCO3-) to Ca2+ ratio; and salinity/conductivity of the used water so as to explain the impact of water quality on the irrigation water using a reliable literature database. The results of the developed models were compared with a commonly used model in literature using visual and statistical parameters. Consequently, the supremacy of the regression-based approach is demonstrated with the average absolute relative deviations (AARDs) of 0.06% for HCO3-/Ca2+ ratio ≤1 and 0.28% for HCO3-/Ca2+ ratio >1. Finally, it should be mentioned that the proposed methods are easy-to-apply and sufficiently accurate which require the less calculations leading to the rapid estimation of sodium adsorption ratio (SAR) in wide range of operational conditions. Inderscience Publishers - linking academia, business and industry through research

Title: Robust computational modelling of the sodium adsorption ratio using regression analysis and support vector machine

Authors: Alireza Rostami; Milad Arabloo; Alibakhsh Kasaeian; Khalil Shahbazi

Addresses: Department of Petroleum Engineering, Petroleum University of Technology (PUT), P.O. Box 6198144471, Ahwaz, Iran ' Department of Petroleum Engineering, Petroleum University of Technology (PUT), P.O. Box 6198144471, Ahwaz, Iran ' Faculty of New Sciences and Technologies, University of Tehran, P.O. Box 1439956191, Tehran, Iran ' Department of Petroleum Engineering, Petroleum University of Technology (PUT), P.O. Box 6198144471, Ahwaz, Iran

Abstract: In present study, two new methods including least-square support vector machine (LSSVM) and regression-based model, were created for accurate estimation of the adsorption ratio of sodium in terms of ionic concentrations of calcium (Ca²⁺), magnesium (Mg²⁺), and sodium (Na⁺); the bicarbonate (HCO₃^-) to Ca²⁺ ratio; and salinity/conductivity of the used water so as to explain the impact of water quality on the irrigation water using a reliable literature database. The results of the developed models were compared with a commonly used model in literature using visual and statistical parameters. Consequently, the supremacy of the regression-based approach is demonstrated with the average absolute relative deviations (AARDs) of 0.06% for HCO₃^-/Ca²⁺ ratio ≤1 and 0.28% for HCO₃^-/Ca²⁺ ratio >1. Finally, it should be mentioned that the proposed methods are easy-to-apply and sufficiently accurate which require the less calculations leading to the rapid estimation of sodium adsorption ratio (SAR) in wide range of operational conditions.

Keywords: SAR; sodium adsorption ratio; irrigation water; salinity; least square support vector machine; error analysis; sensitivity analysis; regression model.

DOI: 10.1504/IJDS.2020.113051

International Journal of Data Science, 2020 Vol.5 No.3, pp.203 - 228

Received: 23 Mar 2020
Accepted: 04 May 2020
Published online: 16 Feb 2021 *

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Title: Robust computational modelling of the sodium adsorption ratio using regression analysis and support vector machine

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