Incorporation of information entropy theory, artificial neural network, and soft computing models in the development of integrated industrial water quality index

Egbueri, Johnbosco C.

doi:10.1007/s10661-022-10389-x

Cited by 34 publications

(7 citation statements)

References 63 publications

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“…Total hardness is not attributable to a single constituent, as it is based on concentration of calcium and magnesium, hence hardness may be considered as "the effect of alkaline earth cations (Hem, 1989). Total hardness along with alkalinity and pH control the aggressiveness of the water (Egbueri et al, 2022b).…”

Section: Be = ( ) ∑ Cations − ∑ Anionsmentioning

confidence: 99%

WITHDRAWN: Seasonal variation of the quality of groundwater resources for human consumption and industrial purposes in the Central Plain Zone of Punjab, India

Singh

Wani

Egbueri

et al. 2023

Preprint

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Due to environmental pollution, climate change and anthropogenic activities, the judicious use and quality of groundwater for industrial, agricultural, and drinking purposes had garnered a lot of attention. In order to access the seasonal suitability of groundwater on the basis of hydrochemistry and different indices, 196 groundwater samples were collected and analysed for different physicochemical parameters. The results revealed that only 1.02 percent of water samples from pre and post monsoon season exceeded the permissible limits of WHO and BIS guidelines for suitability of drinking water. The TDS content was also within the permissible limits with mean value of 523.7 and 565.6 mg/L for pre and post monsoon seasons, respectively. The concentration of Ca2+, Mg2+ were within the permissible limits whereas chloride content (29.1% and 15.3% during pre and post monsoon period, respectively) exceeds the permissible limits. The sulphate ion concentration (mean value of 0.46 meq L-1) also ranged between the permissible limits. Based upon water quality index (WQI), none of the water sample was unfit for drinking purpose whereas synthetic pollution index (SPI) categorized all the samples as moderately polluted during both the seasons. For industrial purpose suitability, the various indices i.e., LSI (39.8 and 30.6 % have high corrosive tendency during pre and post monsoon, respectively), CSMR (98.4 and 100 % have tendency for galvanic corrosion during pre and post monsoon, respectively) and aggressive index (49.5% were severely corrosive during pre-monsoon period) showed quite variable results. Moreover, only 1.0 per cent of the total water samples seem to be strongly affected by salinization during pre-monsoon period.

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Section: Be = ( ) ∑ Cations − ∑ Anionsmentioning

confidence: 99%

WITHDRAWN: Seasonal variation of the quality of groundwater resources for human consumption and industrial purposes in the Central Plain Zone of Punjab, India

Singh

Wani

Egbueri

et al. 2023

Preprint

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“…The results observed in Boji Boji Agbor might be attributable to the fact that zinc, which is a component of roofing sheets, has been carried down into the soil by rainfall before ending up in the subsurface water through leaching over decades (Oyem et al 2015 ). Zinc is commonly used to coat iron, steel, and other metals to prevent rust and corrosion (Egbueri, 2022a ). Zinc levels in soil may be high as a result of incorrect disposal of zinc-containing wastes by metal production firms and power utilities.…”

Section: Resultsmentioning

confidence: 99%

“…Lead in water can originate from residences that have lead service lines that link to the main water line. Homes without lead service lines may nevertheless have galvanized iron pipes, lead-soldered brass/chrome-plated brass faucets, or other plumbing (Egbueri, 2020 , 2022a , 2022b ; Saha et al, 2017 ). Lead in drinking water cannot be seen, tasted, or smelled.…”

Section: Resultsmentioning

confidence: 99%

“…The application of deep learning and data-intelligent models has significantly reduced the cost of monitoring and assessment of water quality. Studies conducted include the prediction of pH in water (Egbueri & Agbasi, 2022b ; Huang et al, 2019 ; Son et al, 2021 ; Stackelberg et al, 2020 ), prediction of TDS in water (Egbueri & Agbasi, 2022b ; Jamei et al, 2020 ; Mehrdadi et al, 2012 ; Salmani & Jajaei, 2016 ), prediction of TH in water (Azad et al, 2018 ; Egbueri & Agbasi, 2022b ; Roy & Majumder, 2018 ), prediction of anions in water (Egbueri, 2021 ; Mousavi & Amiri, 2012 ; Wagh et al, 2017b ; Yesilnacar et al, 2008 ; Zare et al, 2011 ), prediction of cations in water (Aghel et al, 2019 ; Bondarev, 2019 ; Katimon et al, 2018 ; Nhantumbo et al, 2018 ; Subba Rao et al, 2022b ), prediction of metals in water (Alizamir & Sobhanardakani, 2017a , 2017b ; Egbueri, 2021 ; Fard et al, 2017 ; Ozel et al, 2020 ; Rooki et al, 2011 ), and prediction of water quality indices (Chia et al, 2022 ; Egbueri, 2022a , 2022b ).…”

Section: Introductionmentioning

confidence: 99%

“…The general structure of the ANN consists of an input layer, a hidden layer, and an output layer, each with numerous neurons (Diamantopoulou et al, 2005 ; Pandey et al, 2016 ; Rai et al, 2005 ). ANN has been shown to be effective in the prediction of water quality parameters in many regions of the world (Egbueri 2022b ; Irvan et al, 2022 ; Kouadri et al, 2022 ). This tool has also been undoubtedly valuable in studies related to other disciplines.…”

Section: Introductionmentioning

confidence: 99%

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

Agbasi

Egbueri

2023

J. Sediment. Environ.

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Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria, works that have used data-driven algorithms in predicting PTEs in groundwater are scarce. In addition, only a few works have simulated water quality indices using machine learning modelling methods in the region. Therefore, in this study, physicochemical analyses were carried out on groundwater samples in southeastern Nigeria. The laboratory results were used to compute two water quality indices: pollution index of groundwater (PIG) and the water pollution index (WPI), to ascertain groundwater quality. In addition, the physicochemical parameters served as input variables for multiple linear regression (MLR) and artificial neural network (ANN) modelling and prediction of Cr, Fe, Ni, NO 3 − , Pb, Zn, WPI, and PIG. The results of WPI and PIG computation showed that about 30–35% of the groundwater samples were unsuitable for human consumption, whereas 65–70% of the samples were deemed suitable. The insights from the PIG and WPI model also revealed that lead (Pb) was the most influential PTE that degraded the quality of groundwater resources in the research area. The findings of the MLR and ANN models indicated strong positive prediction accuracies ( R 2 = 0.856–1.000) with low modeling errors. The predictive MLR and ANN models of the PIG and WPI generally outperformed those of the PTEs. The models produced in this study predicted the PTEs better compared to previous studies. Thus, this work provides insights into effective water sustainability, management, and protection.

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Exploring the Recent Trends, Progresses, and Challenges in the Application of Artificial Intelligence in Water Quality Assessment and Monitoring in Nigeria: A Systematic Review

Omeka

2024

Environmental Science and Engineering

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Incorporation of information entropy theory, artificial neural network, and soft computing models in the development of integrated industrial water quality index

Cited by 34 publications

References 63 publications

WITHDRAWN: Seasonal variation of the quality of groundwater resources for human consumption and industrial purposes in the Central Plain Zone of Punjab, India

WITHDRAWN: Seasonal variation of the quality of groundwater resources for human consumption and industrial purposes in the Central Plain Zone of Punjab, India

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

Exploring the Recent Trends, Progresses, and Challenges in the Application of Artificial Intelligence in Water Quality Assessment and Monitoring in Nigeria: A Systematic Review

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