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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Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model

Environmental Pollution, Год журнала: 2023, Номер 336, С. 122456 - 122456

Опубликована: Сен. 4, 2023

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed considerable impact on surface WQ. In response, this research aimed assess in Ireland using an advanced WQ model. To achieve goal, six years monitoring data from 2017 2022 were collected for nine indicators Cork Harbour, Ireland, before, during, and after lockdowns. These include pH, temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved (DOX), transparency (TRAN), three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), total oxidized (TON). results showed that lockdown had a significant indicators, particularly TEMP, TON, BOD5. Over study period, most within permissible limit except MRP, with exception during COVID-19. During pandemic, TON DIN decreased, while improved. contrast, COVID-19, at 7% sites deteriorated. Overall, Harbour was categorized as "good," "fair," "marginal" classes over period. Compared temporal variation, improved 17% period Harbour. However, no trend observed. Furthermore, analyzed model's performance assessing indicate model could be effective tool evaluating lockdowns' quality. can provide valuable information decision-making planning protect aquatic ecosystems.

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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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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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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Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches

Heliyon, Год журнала: 2023, Номер 9(9), С. e19668 - e19668

Опубликована: Сен. 1, 2023

Groundwater resources around the world required periodic monitoring in order to ensure safe and sustainable utilization for humans by keeping good status of water quality. However, this could be a daunting task developing countries due insufficient data spatiotemporal resolution. Therefore, research work aimed assess groundwater quality terms drinking irrigation purposes at adjacent part Rooppur Nuclear Power Plant (RNPP) Bangladesh. For achieving aim study, nine samples were collected seasonally (dry wet season) seventeen hydro-geochemical indicators analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), alkalinity (TA), hardness (TH), organic carbon (TOC), bicarbonate (HCO3-), chloride (Cl-), phosphate (PO43-), sulfate (SO42-), nitrite (NO2-), nitrate (NO3-), sodium (Na+), potassium (K+), calcium (Ca2+) magnesium (Mg2+). The present study utilized Canadian Council Ministers Environment index (CCME-WQI) model purposes. In addition, indices EC, TDS, TH, adsorption ratio (SAR), percent (Na%), permeability (PI), Kelley's (KR), hazard (MHR), soluble percentage (SSP), Residual carbonate (RSC) used assessing computed mean CCME-WQI score found higher during dry season (ranges 48 74) than 40 65). Moreover, ranked between "poor" "marginal" categories implying unsuitable human consumption. Like model, majority also demonstrated suitable crop cultivation season. findings indicate that it requires additional care improve programme protecting RNPP area. Insightful information from might useful as baseline national strategic planners protect any emergencies associated with RNPP.

Язык: Английский

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

Environmental Research, Год журнала: 2023, Номер 242, С. 117755 - 117755

Опубликована: Ноя. 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.

Язык: Английский

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Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

Journal of Environmental Management, Год журнала: 2023, Номер 351, С. 119866 - 119866

Опубликована: Дек. 25, 2023

Язык: 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, Год журнала: 2024, Номер 351, С. 119896 - 119896

Опубликована: Янв. 3, 2024

Язык: Английский

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HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators

Journal of Environmental Management, Год журнала: 2024, Номер 352, С. 120091 - 120091

Опубликована: Янв. 15, 2024

Water is a vital resource supporting broad spectrum of ecosystems and human activities. The quality river water has declined in recent years due to the discharge hazardous materials toxins. Deep learning machine have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity forecasting errors, primarily non-linear datasets hyperparameter settings. To address challenges, we developed an innovative HDTO-DeepAR approach predicting indicators. This proposed compared with standalone algorithms, including DeepAR, BiLSTM, GRU XGBoost, using performance metrics such as MAE, MSE, MAPE, NSE. NSE hybrid ranges between 0.8 0.96. Given value's proximity 1, model appears be efficient. PICP values (ranging 95% 98%) indicate that highly reliable Experimental results reveal close resemblance model's predictions actual values, providing valuable insights future trends. comparative study shows suggested surpasses all existing, well-known models.

Язык: Английский

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