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

Journal of Contaminant Hydrology, Journal Year: 2024, Volume and Issue: 269, P. 104480 - 104480

Published: Dec. 10, 2024

Language: Английский

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Analysis and prediction of atmospheric ozone concentrations using machine learning

Frontiers in Big Data, Journal Year: 2025, Volume and Issue: 7

Published: Jan. 15, 2025

Atmospheric ozone chemistry involves various substances and reactions, which makes it a complex system. We analyzed data recorded by Switzerland's National Air Pollution Monitoring Network (NABEL) to showcase the capabilities of machine learning (ML) for prediction concentrations (daily averages) document general approach that can be followed anyone facing similar problems. evaluated artificial neural networks compared them linear as well non-linear models deduced with ML. The main analyses training were performed on atmospheric air from 2016 2023 at NABEL station Lugano-Università in Lugano, TI, Switzerland. As first step, we used techniques like best subset selection determine measurement parameters might relevant concentrations; general, identified these methods agree chemistry. Based results, constructed predict Lugano period between January 1, 2024, March 31, 2024; then, output our actual measurements repeated this procedure two stations situated northern Switzerland (Dübendorf-Empa Zürich-Kaserne). For stations, predictions made aforementioned 2023, December 2023. In most cases, lowest mean absolute errors (MAE) provided model 12 components (different powers combinations NO 2 , X SO non-methane volatile organic compounds, temperature radiation); MAE predicted was low 9 μgm −3 . Zürich Dübendorf, MAEs around 11 13 respectively. tested periods, accuracy approximately 1 Since values are all lower than standard deviations observations conclude using ML very helpful do not necessarily outperform simpler models.

Language: Английский

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Machine Learning Approaches for Assessing Groundwater Quality and Its Implications for Water Conservation in the Sub-tropical Capital Region of India

Water Conservation Science and Engineering, Journal Year: 2025, Volume and Issue: 10(1)

Published: March 10, 2025

Language: Английский

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Automated interpretation of deep learning-based water quality assessment system for enhanced environmental management decisions

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(5)

Published: April 29, 2025

Language: Английский

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Water quality interactions and their synergistic effects on aquaculture performance in Bangladesh: A critical review

Results in Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 102306 - 102306

Published: May 1, 2025

Language: Английский

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Relationships between the spectral characteristics of dissolved organic matter and river ecological health indicators: A case study in the Shichuan River basin on a typical semi-arid and semi-humid region of China

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112836 - 112836

Published: Nov. 14, 2024

Language: Английский

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