Performance analysis of the water quality index model for predicting water state using machine learning techniques

Process Safety and Environmental Protection, Год журнала: 2022, Номер 169, С. 808 - 828

Опубликована: Ноя. 28, 2022

Existing water quality index (WQI) models assess using a range of classification schemes. Consequently, different methods provide number interpretations for the same properties that contribute to considerable amount uncertainty in correct quality. The aims this study were evaluate performance model order classify coastal correctly completely new scheme. Cork Harbour data was used study, which collected by Ireland's environmental protection agency (EPA). In present four machine-learning classifier algorithms, including support vector machines (SVM), Naïve Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), utilized identify best predicting classes widely seven WQI models, whereas three are recently proposed authors. KNN (100% 0% wrong) XGBoost (99.9% 0.1% algorithms outperformed accurately models. validation results indicate outperformed, accuracy (1.0), precision (0.99), sensitivity specificity F1 (0.99) score, predict Moreover, compared higher prediction accuracy, precision, sensitivity, specificity, score found weighted quadratic mean (WQM) unweighted root square (RMS) respectively, each class. findings showed WQM RMS could be effective reliable assessing terms classification. Therefore, helpful providing accurate information researchers, policymakers, research personnel monitoring more effectively.

Язык: Английский

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Assessing optimization techniques for improving water quality model

Journal of Cleaner Production, Год журнала: 2022, Номер 385, С. 135671 - 135671

Опубликована: Дек. 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.

Язык: 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, Год журнала: 2023, Номер 74, С. 101991 - 101991

Опубликована: Янв. 18, 2023

Язык: Английский

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Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107441 - 107441

Опубликована: Сен. 1, 2023

Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, treatment outcomes. In this era of Artificial Intelligence (AI) models, becomes vital to ensure safe decision-making field. Therefore, review focuses on application techniques machine deep learning models healthcare. A systematic literature was conducted using Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were predominant technique quantification Fuzzy systems being second most used approach. Regarding emerged as prevalent approach, finding nearly all aspects imaging. Most studies reported paper focused images, highlighting compared models. Interestingly, we observed a scarcity applying physiological signals. Thus, future research should prioritize investigating these Overall, our highlights significance integrating applications This can provide valuable insights practical solutions manage real-world data, ultimately improving accuracy reliability diagnoses recommendations.

Язык: Английский

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Optimization of water quality index models using machine learning approaches

Water Research, Год журнала: 2023, Номер 243, С. 120337 - 120337

Опубликована: Июль 11, 2023

Язык: 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, Год журнала: 2023, Номер 879, С. 162998 - 162998

Опубликована: Март 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

Язык: Английский

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A sophisticated model for rating water quality

The Science of The Total Environment, Год журнала: 2023, Номер 868, С. 161614 - 161614

Опубликована: Янв. 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.

Язык: Английский

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

Journal of Environmental Management, Год журнала: 2023, Номер 344, С. 118368 - 118368

Опубликована: Июнь 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.

Язык: 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, Год журнала: 2023, Номер 23, С. 101049 - 101049

Опубликована: Ноя. 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.

Язык: Английский

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A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis

Engineering With Computers, Год журнала: 2023, Номер 40(3), С. 1501 - 1516

Опубликована: Авг. 8, 2023

Abstract There is an increasing interest in creating high-resolution 3D subsurface geo-models using multisource retrieved data, i.e., borehole, geophysical techniques, geological maps, and rock properties, for emergency managements. However, dedicating meaningful, thus interpretable views from such integrated heterogeneous data requires developing a new methodology convenient post-modeling analyses. To this end, the current paper hybrid ensemble-based automated deep learning approach modeling of bedrock proposed. The uncertainty then was quantified novel ensemble randomly deactivating process implanted on jointed weight database. applicability capturing optimum topology validated by geo-model laser-scanned bedrock-level Sweden. In comparison with intelligent quantile regression traditional geostatistical interpolation algorithms, proposed showed higher accuracy visualizing post-analyzing model. Due to use multi-source presented here subsequently created model can be representative reconcile geoengineering applications.

Язык: Английский

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