Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning algorithms

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

Язык: Английский

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Groundwater for drinking and sustainable agriculture and public health hazards of nitrate: developmental and sustainability implications for an arid aquifer system

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104160 - 104160

Опубликована: Янв. 1, 2025

Язык: Английский

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Tailings Dam Failures: A Critical Evaluation of Current Policies and Practices

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103973 - 103973

Опубликована: Янв. 1, 2025

Язык: Английский

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Impact of Nutrient Dynamics on Chlorophyll-a Concentrations in Non-Interconnected Lakes: A Study in the Vellore and Chennai Region

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104315 - 104315

Опубликована: Фев. 1, 2025

Язык: Английский

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Using the Forel-Ule index (FUI) to track the water quality of subsidence water bodies across the life cycle of coal mining in eastern China

Journal of Environmental Management, Год журнала: 2025, Номер 374, С. 124037 - 124037

Опубликована: Янв. 8, 2025

Язык: Английский

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Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104159 - 104159

Опубликована: Янв. 1, 2025

Язык: Английский

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Wastewater quality prediction based on channel attention and TCN-BiGRU model

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(2)

Опубликована: Фев. 1, 2025

Язык: Английский

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Advanced machine learning models for robust prediction of water quality index and classification

Journal of Hydroinformatics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 21, 2025

ABSTRACT This study presents an in-depth analysis of machine learning (ML) techniques for predicting water quality index and classification using a dataset containing metrics such as temperature, specific conductance, salinity, dissolved oxygen, depth, pH, turbidity from multiple monitoring stations. Data preprocessing included imputation missing values, feature scaling, categorical encoding, ensuring balanced input features. research evaluated artificial neural networks, decision trees, support vector machines, random forests, XGBoost, long short-term memory (LSTM) networks. Results demonstrate that XGBoost LSTM significantly outperformed other models, with achieving accuracy range 99.07–99.99% attaining R2 0.9999. Compared prior studies, our approach enhances predictive robustness, showcasing advanced generalization capabilities. The proposed models exhibit significant improvements over traditional methods in handling complex, multivariate data, positioning them promising tools prediction environmental management. These findings underscore the potential ML developing reliable, scalable solutions, providing valuable insights policymakers managers dedicated to sustainable resource

Язык: Английский

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A Novel Approach to Sustainable Water Treatment Utilising Cocos nucifera and Luffa acutangula

Desalination and Water Treatment, Год журнала: 2025, Номер unknown, С. 101038 - 101038

Опубликована: Фев. 1, 2025

Язык: Английский

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Selective gas adsorption using graphitic carbon nitride: Exploring the role of molecular descriptors by artificial intelligence frameworks

Journal of Environmental Management, Год журнала: 2025, Номер 376, С. 124432 - 124432

Опубликована: Фев. 10, 2025

Язык: Английский

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