Abstract
Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a deep insight pertaining to rising water quality concerns. The current study investigates the predictive performance of gene expression programming (GEP), artificial neural network (ANN) and linear regression model (LRM) for modeling monthly total dissolved solids (TDS) and specific conductivity (EC) in the upper Indus River at two outlet stations. In total, 30 years of historical water quality data, comprising 360 TDS and EC monthly records, were used for models training and testing. Based on a significant correlation, the TDS and EC modeling were correlated with seven input parameters. Results were evaluated using various performance measure indicators, error assessment and external criteria. The simulated outcome of the models indicated a strong association with actual data where the correlation coefficient above 0.9 was observed for both TDS and EC. Both the GEP and ANN models remained the reliable techniques in predicting TDS and EC. The formulated GEP mathematical equations depict its novelty as compared to ANN and LRM. The results of sensitivity analysis indicated the increasing trend of input variables affecting TDS as HCO3− (22.33%) > Cl− (21.66%) > Mg2+ (16.98%) > Na+ (14.55%) > Ca2+ (12.92%) > SO42− (11.55%) > pH (0%), while, in the case of EC, it followed the trend as HCO3− (42.36%) > SO42−(25.63%) > Ca2+ (13.59%) > Cl− (12.8%) > Na+ (5.01%) > pH (0.61%) > Mg2+ (0%). The parametric analysis revealed that models have incorporated the effect of all the input parameters in the modeling process. The external assessment criteria confirmed the generalized outcome and robustness of the proposed approaches. Conclusively, the outcomes of this study demonstrated that the formulation of AI based models are cost effective and helpful for river water quality assessment, management and policy making.
Highlights
Surface water is the most sensitive and vulnerable resource since its demand increases with the rise in population
The enhanced variant of genetic programming (GP) was developed by Ferreira, C. [35], which is known as gene expression programming (GEP)
A parse tree structure is coded in GEP which overcome the limitations of the Genetic Programing (GP) [36]
Summary
Surface water is the most sensitive and vulnerable resource since its demand increases with the rise in population. The surface water bodies are abundantly available which fulfill multiple needs such as industrial processes, drinking, irrigation, agricultural production and hydroelectricity generation. The streams, lakes and rivers are the most susceptible to pollution, receiving more waste load from other sources attributed to their dynamic nature and easy accessibility. Major processes responsible for water quality degradation include urban waste water, non-treatment sewage discharge, industrial processes, hazardous substances, diffuse pollution from agricultural lands, climatic processes and anthropogenic
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