Study of the effectiveness of using machine learning methods to predict the quality of atmospheric air in cities
DOI:
https://doi.org/10.51301/ce.2024.i1.04Keywords:
air pollution, air quality monitoring, PM2.5, pollutant forecasting, XGBoost, Support Vector Regression (SVR), Random Forest, LSTM, environmental sustainability, machine learning algorithms, atmospheric air monitoring, environmental man-agement, integrated information systems, air quality forecasting, sustainable development, data analysisAbstract
The study examines the use of machine learning methods for air quality monitoring in industrial cities. Modern approaches to data collection, their accuracy, continuity, as well as the strengths and weaknesses of algorithms, are analyzed. A comparison of machine learning methods, their efficiency, and limitations is presented. This work will be valuable for environmental sector specialists, offering recommendations to enhance air quality monitoring.
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Copyright (c) 2024 Computing & Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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