Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models

Muhammad Izhar Shah, Wesam Salah Alaloul, Abdulaziz Alqahtani, Ali Aldrees, Muhammad Ali Musarat and Muhammad Faisal Javed
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Muhammad Izhar Shah: Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
Wesam Salah Alaloul: Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bander Seri Iskandar 32610, Malaysia
Abdulaziz Alqahtani: Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
Ali Aldrees: Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
Muhammad Ali Musarat: Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bander Seri Iskandar 32610, Malaysia
Muhammad Faisal Javed: Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan

Sustainability, 2021, vol. 13, issue 14, 1-20

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 HCO 3 − (22.33%) > Cl − (21.66%) > Mg 2+ (16.98%) > Na + (14.55%) > Ca 2+ (12.92%) > SO 4 2− (11.55%) > pH (0%), while, in the case of EC, it followed the trend as HCO 3 − (42.36%) > SO 4 2− (25.63%) > Ca 2+ (13.59%) > Cl − (12.8%) > Na + (5.01%) > pH (0.61%) > Mg 2+ (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.

Keywords: river water quality; sustainable environment; soft computing; regression analysis; total dissolved solids; specific conductivity; parametric study; variable importance; external validation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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