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Title	Framework Of Air Pollution Assessment In Smart Cities Using Iot With Machine Learning Approach
ID_Doc	27055
Authors	Varade H.P.; Bhangale S.C.; Thorat S.R.; Khatkale P.B.; Sharma S.K.; William P.
Year	2023
Published	Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
DOI	http://dx.doi.org/10.1109/ICAAIC56838.2023.10140834
Abstract	Exhale and inhale filthy air has major health consequences. Air pollution's influence may be mitigated by conducting regular monitoring and keeping a record of it. Government organizations may also take proactive measures to protect the environment by accurately anticipating pollution levels in real time. In future smart cities, we propose using the Internet of Things and machine learning to track pollution levels in the air we breathe. The Pearson correlation test is performed to see whether pollutants and meteorological indicators have a high link. A cloud-centric IoT middleware architecture is used in this research instead of a standard sensor network to gather data from both air pollution and current weather sensors. This means that both reliability and cost have been greatly improved. Sulphur Dioxide (SO2) and Particulate Matter levels were predicted using an Artificial Neural Network (ANN) (PM2.5). The positive results show that ANNs may be used to monitor and forecast air pollution. RMSE values of 0.0128 and 0.0001 for SO2 and PM2.5 were found using our models. © 2023 IEEE.
Author Keywords	Air Pollution; Artificial Neural Network; Environment; IOT; Smart Cities

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