Marine waters assessment using improved water quality model incorporating machine learning approaches

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 344, P. 118368 - 118368

Published: June 24, 2023

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends a number of factors, one the most important is quality water. The index (WQI) model widely used to assess quality, but existing models have uncertainty issues. To address this, authors introduced two new WQI models: weight based weighted quadratic mean (WQM) unweighted root squared (RMS) models. These were in Bay Bengal, using seven indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), molybdate reactive phosphorus (MRP). Both ranked between "fair" categories, with no significant difference models' results. showed considerable variation computed scores, ranging from 68 88 an average 75 for WQM 70 76 72 RMS. did not any issues sub-index or aggregation functions, had high level sensitivity (R2 = 1) terms spatio-temporal resolution waterbodies. study demonstrated that approaches effectively assessed waters, reducing improving accuracy score.

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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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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Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Heliyon, Journal Year: 2024, Volume and Issue: 10(6), P. e27920 - e27920

Published: March 1, 2024

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality ensure usability. The advent of the. Internet Things (IoT) has brought about revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring water (WQ). By employing Machine learning (ML) techniques, this gathered can be analyzed make accurate predictions regarding quality. These predictive insights play crucial role decision-making processes aimed at safeguarding quality, such identifying areas need immediate attention and implementing preventive measures avert contamination. This paper aims provide comprehensive review current state art monitoring, with specific focus on employment IoT wireless technologies ML techniques. study examines utilization range technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, Bluetooth, context Furthermore, it explores application both supervised unsupervised algorithms for analyzing interpreting collected data. In addition discussing art, survey also addresses challenges open research questions involved integrating (WQM).

Language: Английский

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Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Water, Journal Year: 2024, Volume and Issue: 16(2), P. 264 - 264

Published: Jan. 11, 2024

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the water index (IWQI), this research aims evaluate Naama, a region southwest Algeria. Hydrochemical parameters (cations, anions, pH, EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well geospatial representations were used determine groundwater’s suitability study area. In addition, efficient forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), K-Nearest Neighbours (KNN) models implemented. research, 166 samples calculate index. results showed that 42.18% them excellent quality, 34.34% very good 6.63% 9.64% satisfactory, 4.21% considered unsuitable irrigation. On other hand, indicate XGBoost excels accuracy stability, low RMSE (of 2.8272 high R 0.9834. SVR only four inputs (Ca2+, Mg2+, Na+, K) demonstrates notable predictive capability 2.6925 0.98738, while KNN showcases robust performance. distinctions between these have important implications making informed decisions agricultural management resource allocation within region.

Language: Английский

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Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175407 - 175407

Published: Aug. 9, 2024

Language: Английский

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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

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A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102500 - 102500

Published: Jan. 28, 2024

The importance of water quality models has increased as their inputs are critical to the development risk assessment framework for environmental management and monitoring rivers. However, with advent a plethora recent advances in ML algorithms better predictions possible. This study proposes causal effect model by considering climatological such temperature precipitation along geospatial information related agricultural land use factor (ALUF), forest (FLUF), grassland usage (GLUF), shrub (SLUF), urban (ULUF). All these factors included input data, whereas four Stream Water Quality parameters (SWQPs) Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, Dissolved (DO) from 2019 2021 taken outputs predict Godavari River Basin quality. In preliminary investigation, out SWQPs, nitrate's coefficient variation (CV) is high, revealing close association climate practices across sampling stations. authors' earlier study, using single-layer Feed-Forward Neural Network (FFNN) showed improved performance predicting cause linked metrics. To achieve prediction, stacked ANN meta-model nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive (ADB), Decision Tree (DT), Highest (HGB), Light Method (LGBM), (GB), were compared this study. According study's findings, outperformed stand-alone FFNN same dataset superior predictive capabilities terms accuracy forecasting variable interest. For instance, during testing, determination (R2) (BOD) 0.72 0.87. Furthermore, Artificial (ANN) meta that was reinforced (ET) base performed than individual (from R2 = 0.87 0.91 BOD testing). By new framework, effort hyperparameter tuning can be minimized.

Language: 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, Journal Year: 2025, Volume and Issue: 58, P. 102227 - 102227

Published: Feb. 17, 2025

Language: Английский

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Superior photocatalytic and self-cleaning performance of PVDF-TiO₂@NH₂-MIL-125(Ti)/PVA membranes for efficient produced water treatment

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 72, P. 107415 - 107415

Published: March 10, 2025

Language: Английский

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