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Title	Air Quality Prediction In Smart Cities Using Regression Techniques
ID_Doc	7170
Authors	Jayakumar J.; Thangavel H.S.; Pandiyan H.; Ramesh N.K.
Year	2025
Published	AIP Conference Proceedings, 3279, 1
DOI	http://dx.doi.org/10.1063/5.0263189
Abstract	This research endeavors to tackle the growing concerns associated with the deterioration of air quality in smart cities, a consequence of rapid urbanization and increased population density. The primary objective is to develop a precise and timely air quality prediction system, aiming to facilitate proactive measures for pollution control and ultimately improve public well-being. The proposed study takes a comprehensive approach, employing advanced regression techniques to forecast air quality accurately. Robust regression models are constructed using historical air quality data, meteorological parameters, and other relevant environmental variables. The study explores and compares the effectiveness of various regression techniques, including linear regression, polynomial regression, and machine learning algorithms such as Support Vector Regression (SVR) and Random Forest Regression. To ensure adaptability and generalization, the models are trained on a substantial dataset gathered from diverse urban environments. © 2025 Author(s)
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