Smart City Gnosys

Smart city article details

Title	Deep Learning Technologies For The Assessment Of Air Pollution Level Systems In Smart Cities
ID_Doc	17922
Authors	Priscila S.S.; Jayanthiladevi A.
Year	2023
Published	International Journal of System of Systems Engineering, 13, 4
DOI	http://dx.doi.org/10.1504/IJSSE.2023.134433
Abstract	In order to prevent pollution and make sure that air pollution control systems are working as intended, forecasting can provide reliable data on pollution prediction. When analysing the ecosystem in smart cities, it is imperative to update the framework in order to improve daily life. When other factors, such as humidity, temperature, the direction and speed of the wind, as well as precipitation and snowfall, are taken into account, it is much more challenging to comprehend how concentrations of air pollution change over time. In these conditions, it is more difficult to comprehend fluctuations in air pollution concentrations. This paper presents a number of different deep-learning models that, by drawing on a wide variety of characteristics, can assist in the forecasting of environmental air pollution. This paper examines the limitations of the methodologies that are currently being used and provides recommendations for future researchers who are interested in advancing prediction-based research in the field of air pollution. © 2023 Inderscience Enterprises Ltd.. All rights reserved.
Author Keywords	air quality; ANFIS; CO; CO2; community; deep learning; ecosystems; environment; geostatistics; government; long short-term memory unit; LSTM; NO2; patients; PM 10; PM2.5; pollutant; pollution; prediction; SO2

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