article · Water
Water quality in Egypt's Qaroun Lake was assessed using samples collected from sixteen locations across 2018 and 2019. Thirteen physiochemical parameters were analysed using standard water quality and metal pollution indices, supported by multivariate statistics and support vector machine regression. The evaluated indices demonstrated that the lake's surface water is heavily contaminated and unsuitable for aquatic life. Uncontrolled discharges of domestic and industrial wastewater have caused severe aluminium pollution, moderate cadmium and copper contamination, and slight zinc contamination, while rising salinity further accelerates environmental decline. Machine learning regression models successfully predicted the various water quality indices with high statistical accuracy, achieving validation coefficient values between 0.97 and 0.99. The findings highlight an urgent need to treat sewage and drainage inflows to protect the aquatic ecosystem.
Rising populations and unplanned developments place severe pressure on natural aquatic ecosystems through untreated waste disposal. By identifying critical heavy metal contaminants and demonstrating how machine learning can accurately model pollution indices, this research offers environmental managers clearer evidence to justify wastewater treatment infrastructure and protect vital lake habitats.
The work presents an applied predictive modeling approach using support vector machine regression that could assist environmental agencies, water utility operators, and ecological monitoring services in predicting complex water quality indices from basic physiochemical parameters. Because the computational models were tested and validated directly on field data, the methodology is at an applied stage, though turning it into a deployable software tool or monitoring platform would require further software engineering.
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Water quality has deteriorated in recent years as a result of rising population and unplanned development, impacting ecosystem health. The water quality parameters of Qaroun Lake are contaminated to varying degrees, particularly for aquatic life consumption. For that, the objective of this work is to improve the assessments of surface water quality and to determine the different geo-environmental parameters affecting the lake environmental system in Qaroun Lake utilizing the weighted arithmetic water quality index (WAWQI) and four pollution indices (heavy metal pollution index (HPI), metal index (MI), contamination index (Cd), and pollution index (PI), that are enhanced by multivariate analyses as cluster analysis (CA), principal component analysis (PCA), and support vector machine regression (SVMR). Surface water samples were collected at 16 different locations from the lake during years 2018 and 2019. Thirteen physiochemical parameters were measured and used to calculate water quality indices (WQIs). The WQIs of Qaroun Lake such WAWQI, HPI, MI, Cd, PI revealed a different degree of contamination, with respect to aquatic life utilization. The WQIs result revealed that surface water in the lake is unsuitable, high polluted, and seriously affected by pollution for an aquatic environment. The PI findings revealed that surface water samples of Qaroun Lake were significantly impacted by Al, moderately affected by Cd and Cu, and while slightly affected by Zn due to uncontrolled releases of domestic and industrial wastewater. Furthermore, increasing salinity accelerates the deterioration of the lake aquatic environment. Therefore, sewage and drainage wastewater should be treated before discharging into the lake. The SVMR models based on physiochemical parameters presented the highest performance as an alternative method to predict the WQIs. For example, the calibration (Val.) and the validation (Val.) models performed best in assessing the WQIs with R2 (0.99) and with R2 (0.97–0.99), respectively. Finally, a combination of WQIs, CA, PCA, and SVMR approaches could be employed to assess surface water quality in Qaroun Lake.
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DOI: 10.3390/w13162258
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