A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

Water Research, Journal Year: 2022, Volume and Issue: 229, P. 119422 - 119422

Published: Nov. 25, 2022

With the significant increase in WQI applications worldwide and lack of specific application guidelines, accuracy reliability models is a major issue. It has been reported that produce uncertainties during various stages their including: (i) water quality indicator selection, (ii) sub-index (SI) calculation, (iii) weighting (iv) aggregation sub-indices to calculate overall index. This research provides robust statistically sound methodology for assessment model uncertainties. Eight are considered. The Monte Carlo simulation (MCS) technique was applied estimate uncertainty, while Gaussian Process Regression (GPR) algorithm utilised predict at each sampling site. functions were found contribute considerable uncertainty hence affect - they contributed 12.86% 10.27% summer winter applications, respectively. Therefore, selection function needs be made with care. A low less than 1% produced by processes. Significant statistical differences between functions. weighted quadratic mean (WQM) provide plausible coastal waters reduced levels. findings this study also suggest unweighted root means squared (RMS) could potentially used quality. Findings from inform range stakeholders including decision-makers, researchers, agencies responsible monitoring, management.

Language: Английский

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Assessing optimization techniques for improving water quality model

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 385, P. 135671 - 135671

Published: Dec. 19, 2022

In order to keep the "good" status of coastal water quality, it is essential monitor and assess frequently. The Water quality index (WQI) model one most widely used techniques for assessment quality. It consists five components, with indicator selection technique being more crucial components. Several studies conducted recently have shown that use existing results in a significant amount uncertainty produced final due inappropriate selection. present study carried out comprehensive various features (FS) selecting indicators develop an efficient WQI model. This aims analyse effects eighteen different FS techniques, including (i) nine filter methods, (ii) two wrapper (iii) seven embedded methods comparison performance WQI. total, fifteen combinations (subsets) were constructed, values calculated each combination using improvement methodology model's was tested machine-learning algorithms, which validated metrics. indicated tree-based random forest algorithm could be effective terms assessing water. Deep neural network showed better predicting accurately incorporating subset forest.

Language: Английский

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Predicting Water Quality Index (WQI) by feature selection and machine learning: A case study of An Kim Hai irrigation system

Ecological Informatics, Journal Year: 2023, Volume and Issue: 74, P. 101991 - 101991

Published: Jan. 18, 2023

Language: Английский

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Optimization of water quality index models using machine learning approaches

Water Research, Journal Year: 2023, Volume and Issue: 243, P. 120337 - 120337

Published: July 11, 2023

Language: Английский

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A sophisticated model for rating water quality

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 868, P. 161614 - 161614

Published: Jan. 18, 2023

Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve method develop a tool that can be used by environmental regulators abate pollution Ireland. The developed has been associated with adoption of standards formulated waterbodies according framework directive legislation regulator water. consists five identical components, including (i) indicator selection technique is select crucial indicator; (ii) sub-index (SI) function rescaling various indicators' information into uniform scale; (iii) weight estimating values based on relative significance real-time quality; aggregation computing index (WQI) score; (v) score interpretation scheme state quality. IEWQI was Cork Harbour, applied four Ireland, using 2021 data summer winter seasons order evaluate sensitivity terms spatio-temporal resolution waterbodies. efficiency uncertainty were also analysed this research. In different magnitudes domains, shows higher application domains during winter. addition, results reveal architecture may effective reducing avoid eclipsing ambiguity problems. findings study could efficient reliable assessment more accurately any geospatial domain.

Language: Английский

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Assessing and forecasting water quality in the Danube River by using neural network approaches

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 879, P. 162998 - 162998

Published: March 24, 2023

The health and quality of the Danube River ecosystems is strongly affected by nutrients loads (N P), degree contamination with hazardous substances or oxygen depleting substances, microbiological changes in river flow patterns sediment transport regimes. Water index (WQI) an important dynamic attribute characterization quality. WQ scores do not reflect actual condition water We proposed a new forecast scheme for based on following qualitative classes very good (0-25), (26-50), poor (51-75), (76-100) extremely polluted/non-potable (>100). forecasting using Artificial Intelligence (AI) meaningful method protecting public because its possibility to provide early warning regarding harmful pollutants. main objective present study WQI time series data physical, chemical status parameters associated scores. Cascade-forward network (CFN) models, along Radial Basis Function Network (RBF) as benchmark model, were developed from 2011 2017 forecasts produced period 2018-2019 at all sites. nineteen input features represent initial dataset. Moreover, Random Forest (RF) algorithm refines dataset selecting eight considered most relevant. Both datasets are employed constructing predictive models. According results appraisal, CFN models better outcomes (MSE = 0.083/0,319 R-value 0.940/0.911 quarter I/quarter IV) than RBF In addition, show that both could be effective predicting when relevant used variables. Also, CFNs accurate short-term curves which reproduce first fourth quarters (the cold season). second third presented slightly lower accuracy. reported clearly demonstrate successfully they may learn historic determine nonlinear relationships between output

Language: Английский

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Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques

Environmental Monitoring and Assessment, Journal Year: 2023, Volume and Issue: 195(4)

Published: March 8, 2023

Language: Английский

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Marine waters assessment using improved water quality model incorporating machine learning approaches

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 344, P. 118368 - 118368

Published: June 24, 2023

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends a number of factors, one the most important is quality water. The index (WQI) model widely used to assess quality, but existing models have uncertainty issues. To address this, authors introduced two new WQI models: weight based weighted quadratic mean (WQM) unweighted root squared (RMS) models. These were in Bay Bengal, using seven indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), molybdate reactive phosphorus (MRP). Both ranked between "fair" categories, with no significant difference models' results. showed considerable variation computed scores, ranging from 68 88 an average 75 for WQM 70 76 72 RMS. did not any issues sub-index or aggregation functions, had high level sensitivity (R2 = 1) terms spatio-temporal resolution waterbodies. study demonstrated that approaches effectively assessed waters, reducing improving accuracy score.

Language: Английский

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Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach

Groundwater for Sustainable Development, Journal Year: 2023, Volume and Issue: 23, P. 101049 - 101049

Published: Nov. 1, 2023

Groundwater plays a pivotal role as global source of drinking water. To meet sustainable development goals, it is crucial to consistently monitor and manage groundwater quality. Despite its significance, there are currently no specific tools available for assessing trace/heavy metal contamination in groundwater. Addressing this gap, our research introduces an innovative approach: the Quality Index (GWQI) model, developed tested Savar sub-district Bangladesh. The GWQI model integrates ten water quality indicators, including six heavy metals, collected from 38 sampling sites study area. enhance precision assessment, employed established machine learning (ML) techniques, evaluating model's performance based on factors such uncertainty, sensitivity, reliability. A major advancement incorporation metals into framework index model. best authors knowledge, marks first initiative develop encompassing heavy/trace elements. Findings assessment revealed that area ranged 'good' 'fair,' indicating most indicators met standard limits set by Bangladesh government World Health Organization. In predicting scores, artificial neural networks (ANN) outperformed other ML models. Performance metrics, root mean square error (RMSE), (MSE), absolute (MAE) training (RMSE = 0.361; MSE 0.131; MAE 0.262), testing 0.001; 0.00; 0.001), prediction evaluation statistics (PBIAS 0.000), demonstrated superior effectiveness ANN. Moreover, exhibited high sensitivity (R2 1.0) low uncertainty (less than 2%) rating These results affirm reliability novel monitoring management, especially regarding metals.

Language: Английский

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Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model

Chemosphere, Journal Year: 2024, Volume and Issue: 352, P. 141393 - 141393

Published: Feb. 5, 2024

Language: Английский

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