Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Water Research, Journal Year: 2024, Volume and Issue: 255, P. 121499 - 121499

Published: March 20, 2024

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that model produced inconsistent results due to data outliers, which significantly impact reliability accuracy. The present study was carried out assess of outliers on recently developed Irish Water Quality Index (IEWQI) model, relies techniques. To author's best knowledge, no systematic framework for evaluating influence such models. For purposes assessing outlier (WQ) this first initiative research introduce comprehensive approach combines with advanced statistical proposed implemented Cork Harbour, Ireland, evaluate IEWQI model's sensitivity input indicators quality. In order detect outlier, utilized two widely used ML techniques, including Isolation Forest (IF) Kernel Density Estimation (KDE) within dataset, predicting WQ without these outliers. validating results, five commonly measures. performance metric (R2) indicates improved slightly (R2 increased from 0.92 0.95) after removing input. But scores were statistically differences among actual values, predictions 95% confidence interval at p < 0.05. uncertainty also contributed <1% final assessment using both datasets (with outliers). addition, all measures indicated techniques provided reliable can be detecting their impacts model. findings reveal although had architecture, they moderate rating schemes' This finding could improve accuracy as well helpful mitigating eclipsing problem. provide evidence how influenced reliability, particularly since confirmed effective accurately despite presence It occur spatio-temporal variability inherent indicators. However, assesses underscores important areas future investigation. These include expanding temporal analysis multi-year data, examining spatial patterns, detection methods. Moreover, it is essential explore real-world revised categories, involve stakeholders management, fine-tune parameters. Analysing across varying resolutions incorporating additional environmental enhance assessment. Consequently, offers valuable insights strengthen robustness provides avenues enhancing its utility broader applications. successfully adopted affect current Harbour only single year data. should tested various domains response terms resolution domain. Nevertheless, recommended conducted adjust or revise schemes investigate practical effects updated categories. potential recommendations adaptability reveals effectiveness applicability more general scenarios.

Language: Английский

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Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102514 - 102514

Published: Feb. 13, 2024

This study assessed water quality (WQ) in Tongi Canal, an ecologically critical and economically important urban canal Bangladesh. The researchers employed the Root Mean Square Water Quality Index (RMS-WQI) model, utilizing seven WQ indicators, including temperature, dissolve oxygen, electrical conductivity, lead, cadmium, iron to calculate index (WQI) score. results showed that most of sampling locations poor WQ, with many indicators violating Bangladesh's environmental conservation regulations. eight machine learning algorithms, where Gaussian process regression (GPR) model demonstrated superior performance (training RMSE = 1.77, testing 0.0006) predicting WQI scores. To validate GPR model's performance, several measures, coefficient determination (R2), Nash-Sutcliffe efficiency (NSE), factor (MEF), Z statistics, Taylor diagram analysis, were employed. exhibited higher sensitivity (R2 1.0) (NSE 1.0, MEF 0.0) WQ. analysis uncertainty (standard 7.08 ± 0.9025; expanded 1.846) indicates RMS-WQI holds potential for assessing inland waterbodies. These findings indicate could be effective approach waters across study's did not meet recommended guidelines, indicating Canal is unsafe unsuitable various purposes. implications extend beyond contribute management initiatives

Language: Английский

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GIS and fuzzy analytical hierarchy process to delineate groundwater potential zones in southern parts of India

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 25, P. 101110 - 101110

Published: Feb. 13, 2024

Language: Английский

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Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches

Journal of Contaminant Hydrology, Journal Year: 2024, Volume and Issue: 261, P. 104307 - 104307

Published: Jan. 21, 2024

The Rooppur Nuclear Power Plant (RNPP) at Ishwardi, Bangladesh is planning to go into operation within 2024 and therefore, adjacent areas of RNPP gaining adequate attention from the scientific community for environmental monitoring purposes especially water resources management. However, there a substantial lack literature as well datasets earlier years since very little was done beginning RNPP's construction phase. Therefore, this study conducted assess potential toxic elements (PTEs) contamination in groundwater its associated health risk residents part during year 2014–2015. For achieving aim study, samples were collected seasonally (dry wet season) nine sampling sites afterwards analyzed quality indicators such temperature (Temp.), pH, electrical conductivity (EC), total dissolved solid (TDS), hardness (TH) PTEs including Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), Chromium (Cr), Cadmium (Cd) Arsenic (As). This adopted newly developed Root Mean Square index (RMS-WQI) model scenario whereas human assessment utilized quantify toxicity PTEs. In most sites, concentration found higher season than dry Fe, Mn, Cd As exceeded guideline limit drinking water. RMS score mostly classified terms "Fair" condition. non-carcinogenic risks (expressed Hazard Index-HI) revealed that around 44% 89% adults 67% 100% children threshold set by USEPA (HI > 1) possessed through oral pathway season, respectively. Furthermore, calculated cumulative HI throughout period. carcinogenic (CR) PTEs, magnitude decreased following pattern Cr Cd. Although current based on old dataset, findings might serve baseline reduce future hazardous impact power plant.

Language: Английский

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Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 358, P. 120756 - 120756

Published: April 9, 2024

Language: Английский

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Assessment of Drinking Water Quality and Identifying Pollution Sources in a Chromite Mining Region

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 480, P. 136050 - 136050

Published: Oct. 4, 2024

Language: Английский

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Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters

Environmental Processes, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 11, 2025

Language: Английский

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Optimizing Membrane Bioreactor Performance in Wastewater Treatment Using Machine Learning and Meta-Heuristic Techniques

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104626 - 104626

Published: March 1, 2025

Language: Английский

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Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches

Environmental Research, Journal Year: 2023, Volume and Issue: 242, P. 117755 - 117755

Published: Nov. 25, 2023

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) artificial intelligence (AI), enhancing the reliability accuracy trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for (TrC) waters, with focus on Cork Harbour, Ireland, as case study. innovative approach, named Assessment (ATSI) model, comprises three main components: selection pertinent water quality indicators, computation ATSI scores, implementation new scheme. optimize input minimize employed ML techniques, including advanced deep methods. Specifically, CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE MAE 0.01) testing phases. Utilizing linear rescaling interpolation function, calculated scores evaluated model's sensitivity efficiency across diverse application domains, employing metrics such R2, Nash-Sutcliffe (NSE), factor (MEF). The results consistently revealed heightened all domains. Additionally, introduced brand scheme ranking waters. assess spatial sensitivity, applied to four distinct waterbodies comparing assessment outcomes Estuaries Bays Ireland (ATSEBI) System. Remarkably, significant disparities between ATSEBI System were evident except Mulroy Bay. Overall, our significantly enhances assessments marine ecosystems. combined cutting-edge techniques scheme, represents promising avenue evaluating monitoring conditions TrC study also effectiveness assessing various waterbodies, lakes, rivers, more. These findings make substantial contributions field ecosystem management conservation.

Language: Английский

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An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 351, P. 119896 - 119896

Published: Jan. 3, 2024

Language: Английский

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