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Smart city article details

Title	Deep Learning Approaches For Environmental Monitoring In Smart Cities
ID_Doc	17824
Authors	Latha Soundarraj P.; Jamadar V.M.; Rappai S.; Alkhafaji M.A.; Shareef A.M.; Zearah S.A.
Year	2024
Published	Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
DOI	http://dx.doi.org/10.1109/ICONSTEM60960.2024.10568705
Abstract	It introduces a novel integrated environmental monitoring system capable of doing on-the-go measurements. In metropolitan settings, air pollution is one of the most serious environmental threats to human health. The widespread use of automobiles, emissions from manufacturing processes, and the use of fossil fuels for propulsion and power generation have all contributed to this issue. Air quality predictions in smart cities may now be made using deep learning methods, thanks to the widespread adoption of these tools and their continued rapid growth. Particulate Matter (PM) with a width of less than 2.5 m (PM2.5) is one of the most perilous kinds of air pollution. To anticipate the hourly gauge of PM2.5 focus in Delhi, India, we utilized verifiable information of poisons, meteorological information, and PM2.5 fixation in the adjoining stations to make a spatial-worldly element for our CNN-LSTM-based deep learning arrangement. According to our experiments, our 'hybrid CNN-LSTM multivariate' method outperforms all of the above conventional models and allows for more precise predictions. © 2024 IEEE.
Author Keywords	Air pollution; Deep learning; Environmental monitoring; Smart cities

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