Abstract
Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R2, MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R2, MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R2, MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (ENS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an ENS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.
Highlights
Provision of safe and quality drinking water is a major concern in many developing countries due to rapid growth in urbanization and industrialization
The mean concentrations of all the parameters used in this study were lower than guideline value, except turbidity, total dissolved solids (TDS) was chosen as the target parameter considering the salinity problem associated with the water supply systems in the study area
The Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models developed in this study gave an accurate prediction of the observed data (TDS) in GW, surface water (SW) and drinking water (DW), with the R2 consistently greater than 0.850
Summary
Provision of safe and quality drinking water is a major concern in many developing countries due to rapid growth in urbanization and industrialization. An estimate of about 1.8 million people die every year, predominantly in developing countries, due to water-borne diseases and inadequate supply of quality water Total dissolved solid (TDS) is one of the most vital constituents or parameters in assessing the overall suitability and quality of various water supply systems (Atta et al 2018; Li et al 2018; Pan et al 2019). Accurate measurement and prediction of TDS may provide an indication of the salinity (total organic and inorganic dissolved substances) in various water resource systems. Traditional (deterministic and stochastic) models, such as statistical approaches and visual modelling, have been commonly used in literature (Sun and Gui 2015; Tziritis and Lombardo 2017; Chen et al 2018; Karami et al. Vol.:(0123456789)
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