A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning

Water Research, Journal Year: 2020, Volume and Issue: 177, P. 115788 - 115788

Published: April 13, 2020

Language: Английский

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Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Water, Journal Year: 2020, Volume and Issue: 12(12), P. 3300 - 3300

Published: Nov. 24, 2020

Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves combined effects several surface management parameters. Therefore, this work aims to enhance assessment and geochemical controlling mechanisms assess validation networks for using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine (SVMR) Stepwise Multiple Linear (SMLR). A total 110 samples from network cannels during summers 2018 2019 were collected research standard analytical techniques used measure 21 physical chemical The physicochemical properties revealed that major ions concentrations reported in following order: Ca2+ > Na+ Mg2+ K+ alkalinity SO42− Cl− NO3− F−. trace elements Fe Mn B Cr Pb Ni Cu Zn Cd. belongs Ca2+-Mg2+-HCO3− Ca2+-Mg2+-Cl−-SO42− types, under stress silicate weathering reverse ion exchange process. computation WQI values across two years 82% represent high class remaining 18% constitute medium use with respect Irrigation (IWQ) value, while Sodium Percentage (Na%) indicated 96% fell into healthy 4% permissible irrigation. In addition, Absorption Ratio (SAR), Permeability Index (PI), Kelley (KI) Residual Carbonate (RSC) all appropriate use. PCR SVMR accurate robust models predict WQIs both datasets calibration (Cal.) (Val.), R2 varying 0.48 0.99. SMLR presented estimated well, an value ranged 0.66 conclusion, statistical analyses are effective applicable assessing quality. PCR, provided reliable estimates different indices showed highest slopes close 1.00, well minimum RMSE models.

Language: Английский

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Integration of Water Quality Indices and Multivariate Modeling for Assessing Surface Water Quality in Qaroun Lake, Egypt

Water, Journal Year: 2021, Volume and Issue: 13(16), P. 2258 - 2258

Published: Aug. 18, 2021

Water quality has deteriorated in recent years as a result of rising population and unplanned development, impacting ecosystem health. The water parameters Qaroun Lake are contaminated to varying degrees, particularly for aquatic life consumption. For that, the objective this work is improve assessments surface determine different geo-environmental affecting lake environmental system utilizing weighted arithmetic index (WAWQI) four pollution indices (heavy metal (HPI), (MI), contamination (Cd), (PI), that enhanced by multivariate analyses cluster analysis (CA), principal component (PCA), support vector machine regression (SVMR). Surface samples were collected at 16 locations from during 2018 2019. Thirteen physiochemical measured used calculate (WQIs). WQIs such WAWQI, HPI, MI, Cd, PI revealed degree contamination, with respect utilization. unsuitable, high polluted, seriously affected an environment. findings significantly impacted Al, moderately Cd Cu, while slightly Zn due uncontrolled releases domestic industrial wastewater. Furthermore, increasing salinity accelerates deterioration Therefore, sewage drainage wastewater should be treated before discharging into lake. SVMR models based on presented highest performance alternative method predict WQIs. example, calibration (Val.) validation performed best assessing R2 (0.99) (0.97–0.99), respectively. Finally, combination WQIs, CA, PCA, approaches could employed assess Lake.

Language: Английский

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Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 29(38), P. 57147 - 57171

Published: March 28, 2022

Language: Английский

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A novel machine learning application: Water quality resilience prediction Model

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 768, P. 144459 - 144459

Published: Jan. 7, 2021

Language: Английский

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Predicting and analysing the quality of water resources for industrial purposes using integrated data-intelligent algorithms

Groundwater for Sustainable Development, Journal Year: 2022, Volume and Issue: 18, P. 100794 - 100794

Published: May 31, 2022

Language: Английский

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Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Journal of Sedimentary Environments, Journal Year: 2023, Volume and Issue: 8(1), P. 57 - 79

Published: Jan. 19, 2023

Language: Английский

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Tailoring 3D printed concrete through explainable artificial intelligence

Structures, Journal Year: 2023, Volume and Issue: 56, P. 104850 - 104850

Published: July 14, 2023

Language: Английский

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Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 31(41), P. 54178 - 54203

Published: Jan. 9, 2023

Language: Английский

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Smartphone digital image colorimetry couple with chemometric approach for determination of boron in nuts prior to deep eutectic solvent liquid–liquid microextraction: a first application of hybrid chemometrics in SDIC

Analytical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

In this research, a green approach utilizing deep eutectic solvent liquid-liquid microextraction is combined with smartphone digital image colorimetry for the determination of boron in nut samples. A camera was used to capture analyte extract located custom-made colorimetric box. Using ImageJ software, images were split into RGB channels, channel identified as optimum. The distance between cuvette containing and detection determined be 8 cm, while brightness light source 30%. All obtained at 585 nm monochromatic positioned background source. extraction achieved 450 µL 1:4 choline-chloride phenol mole ratio within 60 s another minute centrifugation. limits quantification found 0.02 0.06 µg mL-1, respectively. method linearity, indicated by relative coefficient, greater than 0.9955 standard deviations below 5.4%. Lastly, application chemometrics form artificial intelligence (AI)-based models hybrid machine learning methodologies has been incorporated SDIC quantitative simulation parameters. results gathered showed that these are capable predicting

Language: Английский

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