Driving factor, source identification, and health risk of PFAS contamination in groundwater based on the self-organizing map

Water Research, Год журнала: 2024, Номер 267, С. 122458 - 122458

Опубликована: Сен. 15, 2024

Язык: Английский

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Optimized groundwater quality evaluation using unsupervised machine learning, game theory and Monte-Carlo simulation

Journal of Environmental Management, Год журнала: 2024, Номер 371, С. 122902 - 122902

Опубликована: Ноя. 11, 2024

Язык: Английский

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Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis

Journal of Contaminant Hydrology, Год журнала: 2024, Номер 269, С. 104480 - 104480

Опубликована: Дек. 10, 2024

Язык: Английский

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Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(27), С. 39155 - 39176

Опубликована: Май 29, 2024

Язык: Английский

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Predictive framework of vegetation resistance in channel flow

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 4, 2025

Predicting vegetation-induced flow resistance remains a significant challenge due to the diverse and dynamic nature of river vegetation. Although numerous empirical models are available, they often fail generalize across different environmental conditions, leading inaccurate predictions. This study introduces machine learning-based framework for predicting vegetation resistance, incorporating nine ML methods, including SVM, XGBoost, BP. To improve predictive performance, optimization algorithms such as PSO, WSO, RIME were applied. A comprehensive dataset 490 samples multiple scales was used evaluate model accuracy, indicated: (1) The submergence ratio α Froude number Fr most sensitive parameters affecting Cd, while missing density λ blockage β significantly reduce accuracy; (2) XGBoost outperforms other models, achieving highest accuracy (R2 = 0.9552); (3) stable six parameter deficiency scenarios, with maintaining R2 > 0.85 in all cases. In conclusion, this highlights transformative potential proposed overcoming long-standing challenges estimating vegetated channels. It provides valuable insights sustainable management, bolsters restoration efforts, enhances complex, environments.

Язык: Английский

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Machine Learning-based Model for Groundwater Quality Prediction: A Comprehensive Review and Future Time–Cost Effective Modelling Vision

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Язык: Английский

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Hydrochemical evolution and assessment of groundwater quality in an intensively agricultural area: case study of Chengdu plain, Southwestern China

Environmental Earth Sciences, Год журнала: 2025, Номер 84(8)

Опубликована: Апрель 1, 2025

Язык: Английский

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Potential application of mineral capsules in self-healing cement-based materials under groundwater containing sulfate

Construction and Building Materials, Год журнала: 2024, Номер 457, С. 139471 - 139471

Опубликована: Дек. 1, 2024

Язык: Английский

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Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach

The Science of The Total Environment, Год журнала: 2024, Номер 953, С. 176024 - 176024

Опубликована: Сен. 4, 2024

Язык: Английский

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Spatial distribution and hydrogeochemical processes of high iodine groundwater in the Hetao Basin, China

The Science of The Total Environment, Год журнала: 2024, Номер 953, С. 176116 - 176116

Опубликована: Сен. 7, 2024

Язык: Английский

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