Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

Water, Journal Year: 2022, Volume and Issue: 14(10), P. 1552 - 1552

Published: May 12, 2022

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level existing surface water. This case study aims evaluate performance twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient histogram-based light extreme boosting), three decision tree-based (decision tree, extra trees, random forest), four ANN-based (multilayer perceptron, radial basis function, deep feed-forward neural network, convolutional network), in estimating quality La Buong River Vietnam. Water data at monitoring stations alongside for period 2010–2017 were utilized calculate index (WQI). Prediction ML models was evaluated by using two efficiency statistics (i.e., R2 RMSE). The results indicated that all have good predicting WQI but boosting (XGBoost) has best with highest accuracy (R2 = 0.989 RMSE 0.107). findings strengthen argument especially XGBoost, may be employed prediction a high accuracy, which will further improve management.

Language: Английский

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Assessing optimization techniques for improving water quality model

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 385, P. 135671 - 135671

Published: Dec. 19, 2022

In order to keep the "good" status of coastal water quality, it is essential monitor and assess frequently. The Water quality index (WQI) model one most widely used techniques for assessment quality. It consists five components, with indicator selection technique being more crucial components. Several studies conducted recently have shown that use existing results in a significant amount uncertainty produced final due inappropriate selection. present study carried out comprehensive various features (FS) selecting indicators develop an efficient WQI model. This aims analyse effects eighteen different FS techniques, including (i) nine filter methods, (ii) two wrapper (iii) seven embedded methods comparison performance WQI. total, fifteen combinations (subsets) were constructed, values calculated each combination using improvement methodology model's was tested machine-learning algorithms, which validated metrics. indicated tree-based random forest algorithm could be effective terms assessing water. Deep neural network showed better predicting accurately incorporating subset forest.

Language: Английский

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Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 29(32), P. 48491 - 48508

Published: Feb. 22, 2022

Language: Английский

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Predicting Water Quality Index (WQI) by feature selection and machine learning: A case study of An Kim Hai irrigation system

Ecological Informatics, Journal Year: 2023, Volume and Issue: 74, P. 101991 - 101991

Published: Jan. 18, 2023

Language: Английский

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Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 58, P. 104789 - 104789

Published: Jan. 17, 2024

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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Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements

Environmental Science & Technology, Journal Year: 2023, Volume and Issue: 57(12), P. 4701 - 4719

Published: March 13, 2023

High-frequency water quality measurements in streams and rivers have expanded scope sophistication during the last two decades. Existing technology allows situ automated of constituents, including both solutes particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with hydrological biogeochemical processes, bringing new insights into sources, transport pathways, transformation processes particulates complex catchments along aquatic continuum. Here, we summarize established emerging high-frequency technologies, outline key hydrochemical data sets, review scientific advances focus areas enabled by rapid development rivers. Finally, discuss future directions challenges for using bridge management gaps promoting a holistic understanding freshwater systems catchment status, health, function.

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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Critical review on water quality analysis using IoT and machine learning models

International Journal of Information Management Data Insights, Journal Year: 2024, Volume and Issue: 4(1), P. 100210 - 100210

Published: Jan. 4, 2024

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates various sensors used for water monitoring focuses on index considering multiple physical, chemical, biological parameters. A Review of Internet Things (IoT) research analysis, can help remote parameters using IoT-based that convey assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, IoT system was 95 % accurate in measuring pH, Turbidity, TDS, Temperature, while traditional method only 85 accurate. Also, this study reviewed different A.I. techniques to assess quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, K-nearest neighbors. Compared methods, deep significantly increase accuracy measurements groundwater quality. However, variables, such as caliber training data, metrics' complexity, frequency, will affect accuracy. The geographical information (GIS) is spatial data analysis managing resources. also paper. Based these analyses, has forecasted future sensors, Geospatial Technology, analysis.

Language: 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, 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

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