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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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, Год журнала: 2024, Номер 255, С. 121499 - 121499

Опубликована: Март 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.

Язык: Английский

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

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102514 - 102514

Опубликована: Фев. 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

Язык: 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, Год журнала: 2024, Номер 261, С. 104307 - 104307

Опубликована: Янв. 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.

Язык: 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, Год журнала: 2024, Номер 358, С. 120756 - 120756

Опубликована: Апрель 9, 2024

Язык: Английский

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A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(1), С. 159 - 159

Опубликована: Янв. 13, 2024

Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted the last five years, we focus on application machine learning for predicting water quality. The review begins by presenting latest methodologies acquiring data. Categorizing learning-based predictions into two primary segments—indicator prediction and index prediction—further distinguishes between single-indicator multi-indicator predictions. A meticulous examination each method’s technical details follows. This article explores current cutting-edge research trends algorithms, providing perspective their prediction. It investigates utilization algorithms concludes highlighting significant challenges future directions. Emphasis placed key areas such as hydrodynamic coupling, effective data processing acquisition, mitigating model uncertainty. paper provides detailed present state principal characteristics emerging technologies

Язык: Английский

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Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102227 - 102227

Опубликована: Фев. 17, 2025

Язык: Английский

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AI-driven modelling approaches for predicting oxygen levels in aquatic environments

Journal of Water Process Engineering, Год журнала: 2024, Номер 66, С. 105940 - 105940

Опубликована: Авг. 13, 2024

Язык: Английский

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Predicting and analyzing the algal population dynamics of a grass-type lake with explainable machine learning

Journal of Environmental Management, Год журнала: 2024, Номер 354, С. 120394 - 120394

Опубликована: Фев. 26, 2024

Язык: Английский

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Tracing spatial patterns of lacustrine groundwater discharge in a closed inland lake using stable isotopes

Journal of Environmental Management, Год журнала: 2024, Номер 354, С. 120305 - 120305

Опубликована: Фев. 14, 2024

Язык: Английский

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