Robust computational modelling of the sodium adsorption ratio using regression analysis and support vector machine
by Alireza Rostami; Milad Arabloo; Alibakhsh Kasaeian; Khalil Shahbazi
International Journal of Data Science (IJDS), Vol. 5, No. 3, 2020

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.

Online publication date: Tue, 16-Feb-2021

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