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

Water, Год журнала: 2022, Номер 14(10), С. 1552 - 1552

Опубликована: Май 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.

Язык: Английский

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Assessing optimization techniques for improving water quality model

Journal of Cleaner Production, Год журнала: 2022, Номер 385, С. 135671 - 135671

Опубликована: Дек. 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.

Язык: 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, Год журнала: 2022, Номер 29(32), С. 48491 - 48508

Опубликована: Фев. 22, 2022

Язык: 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, Год журнала: 2023, Номер 74, С. 101991 - 101991

Опубликована: Янв. 18, 2023

Язык: Английский

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Prediction of Water Quality Classification of the Kelantan River Basin, Malaysia, Using Machine Learning Techniques

Water, Год журнала: 2022, Номер 14(7), С. 1067 - 1067

Опубликована: Март 28, 2022

Machine Learning (ML) has been used for a long time and gained wide attention over the last several years. It can handle large amount of data allow non-linear structures by using complex mathematical computations. However, traditional ML models do suffer some problems, such as high bias overfitting. Therefore, this resulted in advancement improvement techniques, bagging boosting approach, to address these problems. This study explores series predict water quality classification (WQC) Kelantan River from 2005 2020. The proposed methodology employed 13 physical chemical parameters 7 that are Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Random Forest Gradient Boosting. Based on analysis, ensemble model Boosting with learning rate 0.1 exhibited best prediction performance compared other algorithms. had highest accuracy (94.90%), sensitivity (80.00%) f-measure (86.49%), lowest error. Total Suspended Solid (TSS) was most significant variable (GB) WQC, followed Ammoniacal Nitrogen (NH3N), Biochemical Oxygen Demand (BOD) Chemical (COD). accurate prediction, results could help improve National Environmental Policy regarding resources continuously improving quality.

Язык: 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, Год журнала: 2024, Номер 58, С. 104789 - 104789

Опубликована: Янв. 17, 2024

Язык: Английский

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

Journal of Cleaner Production, Год журнала: 2023, Номер 406, С. 136885 - 136885

Опубликована: Апрель 3, 2023

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

Environmental Science & Technology, Год журнала: 2023, Номер 57(12), С. 4701 - 4719

Опубликована: Март 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.

Язык: Английский

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

International Journal of Information Management Data Insights, Год журнала: 2024, Номер 4(1), С. 100210 - 100210

Опубликована: Янв. 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.

Язык: Английский

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