Smart City Gnosys

Smart city article details

Title	Air Quality Prediction In Smart Cities Using Regression Techniques
ID_Doc	7171
Authors	Gupta R.; Khandal K.; Kandan M.
Year	2024
Published	Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024, ICNWC 2024
DOI	http://dx.doi.org/10.1109/ICNWC60771.2024.10537556
Abstract	Air pollution in smart cities presents a significant risk to public health and general welfare. Precise air quality forecasting is essential for successful pollution reduction and long-term urban growth. This study performs a comprehensive comparative analysis of air quality prediction utilizing four regression techniques: Random Forest regression, Linear regression, Decision Tree regression, and XGBoost regression. The study seeks to determine the best effective model by evaluating criteria such as R2 measurements and Mean Absolute Error.The results demonstrate that the XGBoost regression method surpasses competing algorithms in terms of efficiency and accuracy. Moreover, the use of cloud computing technologies has greatly enhanced the implementation speed of these tactics. Utilizing distributed computing resources allows for real-time air quality forecasts, facilitating quick decision-making and proactive measures to combat air pollution incidents.This work enhances air quality prediction methods in smart cities by highlighting the effectiveness of the XGBoost regression algorithm. The study highlights the crucial need of utilizing advanced regression techniques and cloud computing to improve the precision and effectiveness of air quality forecasts, aiding in proactive efforts to address air pollution and create healthier urban settings. © 2024 IEEE.
Author Keywords	Air quality prediction; Cloud computing integration; Decision Tree regression; Linear regression; Mean Absolute Error; R2 measures; Random Forest regression; Real-time forecastin; Regression techniques; Smart cities; XGBoost

Similar Articles

Id	Similarity	Authors	Title	Published
15028	0.962	Al-Eidi S.; Amsaad F.; Darwish O.; Tashtoush Y.; Alqahtani A.; Niveshitha N.	Comparative Analysis Study For Air Quality Prediction In Smart Cities Using Regression Techniques	IEEE Access, 11 (2023)
7168	0.936	Niveshitha N.; Amsaad F.; Jhanjhi N.Z.	Air Quality Prediction In Smart Cities Using Cloud Machine Learning	2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023 (2023)
15001	0.931	Sharma G.; Khurana S.; Saina N.; Shivansh; Gupta G.	Comparative Analysis Of Machine Learning Techniques In Air Quality Index (Aqi) Prediction In Smart Cities	International Journal of System Assurance Engineering and Management, 15, 7 (2024)
7170	0.919	Jayakumar J.; Thangavel H.S.; Pandiyan H.; Ramesh N.K.	Air Quality Prediction In Smart Cities Using Regression Techniques	AIP Conference Proceedings, 3279, 1 (2025)
839	0.915	Garg K.D.; Gupta M.; Sharma B.; Dhaou I.B.	A Comparison Of Regression Techniques For Prediction Of Air Quality In Smart Cities	1st International Conference in Advanced Innovation on Smart City, ICAISC 2023 - Proceedings (2023)
15000	0.91	Ameer, S; Shah, MA; Khan, A; Song, HB; Maple, C; Ul Islam, S; Asghar, MN	Comparative Analysis Of Machine Learning Techniques For Predicting Air Quality In Smart Cities	IEEE ACCESS, 7 (2019)
27176	0.9	Kant S.	From Data To Decision-Making: Utilizing Decision Tree For Air Quality Monitoring In Smart Urban Areas	International Journal of Information Technology (Singapore), 17, 1 (2025)
41678	0.898	Banga A.; Ahuja R.; Sharma S.C.	Performance Analysis Of Regression Algorithms And Feature Selection Techniques To Predict Pm2.5 In Smart Cities	International Journal of System Assurance Engineering and Management, 14 (2023)
7169	0.896	Iskandaryan D.; Ramos F.; Trilles S.	Air Quality Prediction In Smart Cities Using Machine Learning Technologies Based On Sensor Data: A Review	Applied Sciences (Switzerland), 10, 7 (2020)
7146	0.892	Mishra A.; Jalaluddin Z.M.; Mahamuni C.V.	Air Quality Analysis And Smog Detection In Smart Cities For Safer Transport Using Machine Learning (Ml) Regression Models	Proceedings - 2022 IEEE 11th International Conference on Communication Systems and Network Technologies, CSNT 2022 (2022)