Support vector regression and ANN approach for predicting the ground water quality

Abstract

The current study investigates the potential of well-known artificial neural network (ANN), Support vector regression (SVR), multilinear and multi-nonlinear regression techniques to predict total dissolve solids (TSO) and electrical conductivity (ECO), which are essential water quality indicators. To develop the anticipated models, seven effective parameters: Ca2+ Mg2+ Na+ Cl- SO42- HCO3- and pH were used as input variables. The external validation criteria were employed to address the modeling overfitting. The outcome of the study demonstrated a strong association between experimental and models predicted data. The coefficient of determination was 0.97, 0.96, 0.92, and 0.94 for SVR, ANN, MLR, and MNLR models, respectively. The lowest error value of 5.37 and 7.92 was attained by SVR model for training and testing data, respectively. Performance of the proposed techniques showed relative dominance of SVR compared to ANN, MLR and MNLR. Sensitivity analysis demonstrated that the HCO3- is the most sensitive parameter for both TSO and ECO followed by Cl- and SO42-. The models assessment on external criteria ensured generalized results. Conclusively, the outcome of the present research indicated that formulation of machine learning models for prediction of water quality parameters are cost effective and helpful in river water quality assessment, management and policy making.