Smart City Gnosys
Smart city article details
| Title | Smart Air Quality Monitoring And Prediction System With Data-Driven Insights |
|---|---|
| ID_Doc | 49106 |
| Authors | Shanmugapriya C.; Kothai E. |
| Year | 2025 |
| Published | Proceedings of IEEE International Conference on Signal Processing,Computing and Control |
| DOI | http://dx.doi.org/10.1109/ISPCC66872.2025.11039371 |
| Abstract | Air pollution has serious consequences for people's health, the environment, and the world's economy. Using critical environmental variables like temperature (T), humidity (H), visibility (VV), wind speed (V), maximum wind speed (VM), particulate matter (PM2.5), sea level pressure (SLP), and maximum and minimum temperatures, this study introduces a machine learning-based Air Quality Index (AQI) prediction system. The system utilizes a Flask-based API to facilitate user interaction by accepting these environmental inputs, processing them through a Random Forest Regressor model optimized using Randomized Search Cross-Validation. This approach ensures accurate and efficient predictions of AQI levels. Exploratory Data Analysis (EDA), including correlation heatmaps, density plots, and pairwise visualizations, is employed to uncover relationships and dependencies between features. The most important elements influencing AQI may be identified by feature importance analysis utilizing the Extra Trees Regressor. This allows for focused interventions to be implemented. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are robust performance measures that are used to analyze the model in order to guarantee accurate outcomes. The technology does more than just forecast pollution levels; it also provides concrete suggestions on how to improve air quality according to those levels. These recommendations include encouraging public transportation, enforcing stricter industrial emission controls, increasing urban greenery, and adopting renewable energy solutions. By integrating real-time data input through an API, advanced machine learning algorithms, and tailored improvement suggestions, this system equips policymakers and environmentalists with valuable insights for air pollution mitigation, contributing to sustainable urban development and improved public health outcomes. © 2025 IEEE. |
| Author Keywords | Air Pollution Prediction; Air Quality Index Prediction; Big Data in Environmental Monitoring; Environmental Sensing; Internet of Things; Real-time Air Quality Analysis; Smart Air Quality Monitoring; Smart City Air Monitoring; Wireless Sensor Networks |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
2458  | 0.916 | Swamynathan S.; Sneha N.; Ramesh S.P.; Niranjana R.; Ponkumar D.D.N.; Saravanakumar R. | A Machine Learning Approach For Predicting Air Quality Index In Smart Cities | Proceedings of 5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024 (2024) |
| 15001 | 0.915 | 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) |
| 839 | 0.913 | 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) |
| 7168 | 0.91 | 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) |
| 7158 | 0.905 | Shree A.N.R.; Shankaramma; Reddy C. | Air Quality Management In Smart Cities By Leveraging Machine Learning Techniques | 3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024 - Proceedings (2024) |
| 42245 | 0.904 | Faydi M.; Zrelli A.; Ezzedine T. | Pm2.5 Prediction Using Deep Learning Models | 17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023 - Proceedings (2023) |
| 15028 | 0.904 | 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) |
| 10552 | 0.903 | Neo E.X.; Hasikin K.; Lai K.W.; Mokhtar M.I.; Azizan M.M.; Hizaddin H.F.; Razak S.A.; Yanto | Artificial Intelligence-Assisted Air Quality Monitoring For Smart City Management | PeerJ Computer Science, 9 (2023) |
| 2472 | 0.902 | Mahalingam U.; Elangovan K.; Dobhal H.; Valliappa C.; Shrestha S.; Kedam G. | A Machine Learning Model For Air Quality Prediction For Smart Cities | 2019 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2019 (2019) |
| 29779 | 0.901 | Mohammed Q.H.; Namburu A. | Hybrid Model And Framework For Predicting Air Pollutants In Smart Cities | Journal of Engineering and Sustainable Development, 28, 3 (2024) |