Prediction of the sodium absorption ratio using data-driven models: a case study in Iran

Abstract

ABSTRACTIn this investigation, two data-driven models, i.e., Gaussian Process (GP) and Support Vector Machine (SVM), were used to predict the sodium absorption ratio (SAR) in three sub-watersheds (Khorramabad, Biranshahr, and Alashtar) in Iran. A comparison was also done with these data-driven models with Artificial Neural Network (ANN). The parameters total dissolved solids, electrical conductivity, pH value, CO3, HCO3, chlorine (Cl), SO4, calcium (Ca), magnesium (Mg), sodium (Na), and potassium (K) were used as input variables and SAR as output. For SVM and GP regression, two kernel functions (radial-based kernel and Person VII kernel function) were used. The results from this investigation suggest that the ANN model (correlation coefficient [CC], root mean square error [RMSE], Nash–Sutcliffe coefficient of efficiency [NSC], and mean absolute relative error [MARE] = 0.9966, 0.0286, 0.9906, and 0.0194) is more precise as compared to the GP (CC, RMSE, NSC, and MARE = 0.9570, 0.2982, 0.8288, and 0.3705) and SVM (CC, RMSE, NSC, and MARE = 0.9948, 0.0365, 0.9847, and 0.063). Among GP and SVM, SVM with PUK kernel is more accurate for estimating the SAR of the watershed. Thus, ANN is a technique which could be used for predicting the SAR for given study area.