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.
International Journal of Data Science, 2020 Vol.5 No.3, pp.203 - 228
Received: 23 Mar 2020
Accepted: 04 May 2020
Published online: 30 Jan 2021 *