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Machine learning has been a popular choice for air quality forecasting because it is good at dealing with nonlinear problems. Dai et al. set up a hybrid model by using a multilayer perception that could predict the concentration and fluctuation in different regions more effectively.
Jan 18, 2023
Oct 1, 2023 · The developed methodology is applicable to estimate pollutant concentrations at other locations.
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Jan 24, 2024 · Abstract. With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed ...
Jan 30, 2023 · A widely used machine learning method (SVR) is used to quantify pollutant and particle levels and predict the air quality index [2].
There have been numerous studies conducted on developing air quality prediction and forecasting models using machine learning to control air pollution.
Prediction of Air Pollutants Using Supervised Machine Learning. Abstract: Air pollution is a severe problem in areas where population density is high such as ...
The machine learning approach is quite precise and consistently predicts the AQI under all environmental circumstances. Machine learning enables us to produce ...
Dec 22, 2021 · Several P M 2.5 prediction methods are developed by researchers based on statistical models and machine learning techniques. Recently, the ...
Their ANN model achieved an accuracy of 92.3%, outperforming all other tested algorithms. The work presented in this paper focuses on the development of AQI ...
Improving 3-day deterministic air pollution forecasts using machine ...
acp.copernicus.org › articles
Jan 19, 2024 · This paper aims to demonstrate how ML can improve the 1, 2, and 3 d deterministic forecasts of several critical urban air pollutants: ...
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