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

摘要

ABSTRACT In 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, CO_(3), HCO_(3), chlorine (Cl), SO_(4), 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.