Application of machine learning in groundwater quality modeling - A comprehensive review

Water Research, Journal Year: 2023, Volume and Issue: 233, P. 119745 - 119745

Published: Feb. 16, 2023

Language: Английский

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Hydrogeochemical Evaluation of Groundwater Aquifers and Associated Health Hazard Risk Mapping Using Ensemble Data Driven Model in a Water Scares Plateau Region of Eastern India

Exposure and Health, Journal Year: 2022, Volume and Issue: 15(1), P. 113 - 131

Published: April 23, 2022

Language: Английский

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Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 406, P. 136885 - 136885

Published: April 3, 2023

Language: 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, 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

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Water Quality Evaluation and Prediction Using Irrigation Indices, Artificial Neural Networks, and Partial Least Square Regression Models for the Nile River, Egypt

Water, Journal Year: 2023, Volume and Issue: 15(12), P. 2244 - 2244

Published: June 15, 2023

Water quality is identically important as quantity in terms of meeting basic human needs. Therefore, evaluating the surface-water and associated hydrochemical characteristics essential for managing water resources arid semi-arid environments. present research was conducted to evaluate predict agricultural purposes across Nile River, Egypt. For that, several irrigation indices (IWQIs) were used, along with an artificial neural network (ANN), partial least square regression (PLSR) models, geographic information system (GIS) tools. The physicochemical parameters, such T °C, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, NO3−, measured at 51 locations. As a result, ions contents following: Ca2+ > Na+ Mg2+ K+ HCO3− Cl− SO42− NO3− reflecting Ca-HCO3 mixed Ca-Mg-Cl-SO4 types. index (IWQI), sodium adsorption ratio (SAR), percentage (Na%), soluble (SSP), permeability (PI), magnesium hazard (MH) had mean values 92.30, 1.01, 35.85, 31.75, 72.30, 43.95, respectively. instance, IWQI readings revealed that approximately 98% samples inside no restriction category, while 2% fell within low area irrigation. ANN-IWQI-6 model’s six indices, R2 0.999 calibration (Cal.) 0.945 validation (Val.) datasets, are crucial predicting IWQI. rest models behaved admirably SAR, Na%, SSP, PI, MR Cal. Val. 0.999. findings ANN PLSR effective methods assist decision plans. To summarize, integrating features, WQIs, ANN, PLSR, GIS tools suitability offers complete image sustainable development.

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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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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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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Groundwater Quality Assessment Using EWQI With Updated Water Quality Classification Criteria: A Case Study in and Around Zhouzhi County, Guanzhong Basin (China)

Exposure and Health, Journal Year: 2022, Volume and Issue: 15(4), P. 825 - 840

Published: Dec. 9, 2022

Language: Английский

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Combination of discretization regression with data-driven algorithms for modeling irrigation water quality indices

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102093 - 102093

Published: April 1, 2023

Language: Английский

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