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

---

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

Environmental Pollution, Journal Year: 2023, Volume and Issue: 336, P. 122456 - 122456

Published: Sept. 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.

Language: Английский

---

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

---

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

---

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

---

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

---

Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan

Water, Journal Year: 2024, Volume and Issue: 16(7), P. 941 - 941

Published: March 25, 2024

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising quality safe drinking effective irrigation. This research primarily focused on employing Water Quality Index (WQI) gauge water’s appropriateness these purposes. However, generation an accurate WQI can prove time-intensive owing potential errors sub-index calculations. In response this challenge, artificial intelligence (AI) forecasting model was devised, aiming streamline process while mitigating errors. The study collected 422 data samples from Mirpurkash, city nestled province Sindh, comprehensive exploration region’s attributes. Furthermore, probed into unraveling interdependencies amidst variables physiochemical analysis water. Diverse machine learning classifiers were employed prediction, findings revealing that Random Forest Gradient Boosting lead 95% 96% accuracy, followed closely by SVM at 92%. KNN exhibits accuracy rate 84%, Decision Trees achieve 77%. Traditional assessment methods are time-consuming error-prone; transformative approach using addresses limitations. addition conducted uncertainty models R-factor, providing insights reliability consistency predictions. dual approach, combining prediction assessment, contributes more understanding Mirpurkash enhances decision-making processes related utilization.

Language: Английский

---

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Environmental Pollution, Journal Year: 2024, Volume and Issue: 347, P. 123771 - 123771

Published: March 15, 2024

Language: Английский

---

Surface water quality evaluation of Mahanadi and its Tributary Katha Jodi River, Cuttack District, Odisha, using WQI, PLSR, SRI, and geospatial techniques

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(2)

Published: Jan. 23, 2025

Language: Английский

---

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

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119866 - 119866

Published: Dec. 25, 2023

Language: Английский

---