WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 21, 2022
Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities
Authors: Kamel Maaloul, Lejdel Brahim
Abstract: Ambient air pollution is the most harmful environmental risk to health. As urban air quality improves, health costs from air pollution-related diseases diminish. This is why air pollution is a major challenge for the public and government around the world. Deployment of the Internet of Things-based sensors has considerably changed the dynamics of predicting air quality. Air pollution can be predicted using machine learning algorithms Data-based sensors in the context of smart cities. In this paper, we performed pollution forecasting using machine learning techniques while presenting a comparative study to determine the best model to accurately predict air quality. Random Forest is an efficient algorithm capable of detecting air quality.
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Keywords: Air Quality, Machine Learning, Random Forest, Smart Cities, Linear Regression, IOT
Pages: 248-256
DOI: 10.37394/23205.2022.21.30
WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 21, 2022, Art. #30