Machine Learning-Based Analysis of Air and Noise Pollution Using Support Vector Machines
Keywords:
Air pollution, Internet of Things (IoT), Machine learning, Noise pollution, Support vector machinesAbstract
Air and noise pollution represent major environmental challenges in modern urban ecosystems, contributing to significant public health risks and sustainability concerns. With the advent of the Internet of Things (IoT), real-time environmental sensing has become increasingly feasible, offering continuous and high-resolution monitoring of pollution indicators. This study presents an IoT-based air and noise pollution monitoring framework integrated with Machine Learning (ML) techniques to classify Air Quality Index (AQI) levels. Drawing from recent advancements in environmental data analytics, the proposed system utilizes Support Vector Machines (SVM) for efficient AQI classification, leveraging real-time data streams captured from a distributed network of sensors. The implementation demonstrates the potential of SVM in handling nonlinear, high-dimensional environmental data, achieving a classification accuracy of 96%, with strong precision and recall for dominant AQI categories. The model also provides effective classification of qualitative noise levels with visually interpretable SVM-based decision boundaries using PCA-reduced components. Furthermore, the study highlights the role of intelligent sensing in smart cities for proactive pollution control and public health management. The results validate that the integration of IoT and ML can significantly enhance the reliability, scalability, and responsiveness of urban environmental monitoring systems, thereby supporting timely decision-making and sustainable urban development.
References
M. M. Soto-Cordova, M. Medina-De-La-Cruz, and A. Mujaico-Mariano, "An IoT-based urban areas air quality monitoring prototype," International Journal of Advanced Computer Science and Applications, vol. 11, no. 9, pp. 711–716, 2020, doi: https://doi.org/10.14569/IJACSA.2020.0110985
M. Malhotra, S. Walia, C. C. Lin, I. K. Aulakh, and S. Agarwal, "A systematic scrutiny of artificial intelligence-based air pollution prediction techniques, challenges, and viable solutions," Journal of Big Data, vol. 11, no. 1, p. 142, Oct. 2024, doi: https://doi.org/10.1186/s40537-024-01002-8
H. M. Ken, M. Behjati, A. S. Rafsanjani, S. Aslam, Y. K. Meng, A. P. Majeed, and Y. Zheng, “Advancing air quality monitoring: TinyML-based real-time ozone prediction with cost-effective edge devices,” in Proc. Int. Conf. Intelligent Manufacturing and Robotics, Singapore: Springer Nature Singapore, Aug. 2024, pp. 502–512. doi: https://doi.org/10.1007/978-981-96-3949-6_42
Y. Özüpak, F. Alpsalaz, and E. Aslan, "Air quality forecasting using machine learning: Comparative analysis and ensemble strategies for enhanced prediction," Water, Air, & Soil Pollution, vol. 236, no. 7, p. 464, Jul. 2025, doi: https://doi.org/10.1007/s11270-025-08122-8
A. Adhikari and C. M. Hussain, "From detection to solution: A review of machine learning in PM2.5 sensing and sustainable green mitigation approaches (2021–2025)," Processes, vol. 13, no. 7, p. 2207, Jul. 2025, doi: https://doi.org/10.3390/pr13072207
A. Miletić, P. Lukovac, T. Naumović, D. Stojanović, and A. Labus, “A data streaming architecture for air quality monitoring in smart cities,” Athens Journal of Technology & Engineering, vol. 10, no. 4, pp. 215–226, 2023. Available: https://www.athensjournals.gr/technology/2023-10-4-2-Miletic.pdf
H. Karnati, "IoT-based air quality monitoring system with machine learning for accurate and real-time data analysis," Arxiv preprint arXiv:2307.00580, Jul. 2, 2023. Available: https://arxiv.org/abs/2307.00580
K. S. Krishna, T. Satish, and J. Mishra, "Machine learning-based IoT air quality and pollution detection," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, pp. 62–76, 2023, doi: https://doi.org/10.17762/ijritcc.v11i2s.6036
J. Jo, B. Jo, J. Kim, S. Kim, and W. Han, “Development of an IoT‐based indoor air quality monitoring platform,” Journal of Sensors, vol. 2020, no. 1, art. no. 8749764, 2020, doi: https://doi.org/10.1155/2020/8749764
C. Banciu, A. Florea, and R. Bogdan, “Monitoring and predicting air quality with IoT devices,” Processes, vol. 12, no. 9, p. 1961, Sep. 2024, doi: https://doi.org/10.3390/pr12091961
G. N. Harish, R. Asharani, and R. Nayana, "IoT-based air pollution monitoring and data analytics using machine learning approach," World Journal of Advanced Research and Reviews, vol. 12, no. 1, pp. 521–528, 2021, doi: https://doi.org/10.30574/wjarr.2021.12.1.0411
M. Dhanalakshmi and V. Radha, "Discretized Linear Regression and Multiclass Support Vector Based Air Pollution Forecasting Technique," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 315-323, 2022. Available: https://arxiv.org/abs/2211.15095
A. K. Hassan, M. S. Saraya, A. M. Ali-Eldin, and M. M. Abdelsalam, "Low-cost IoT air quality monitoring station using cloud platform and blockchain technology," Applied Sciences, vol. 14, no. 13, p. 5774, Jul. 2024, doi: https://doi.org/10.3390/app14135774
N. Bandara, S. Hettiarachchi, and P. Athukorala, “Airspec: An IoT-empowered air quality monitoring system integrated with a machine learning framework to detect and predict defined air quality parameters,” Arxiv preprint arXiv:2111.14125, Nov. 2021. Available: https://arxiv.org/abs/2111.14125
Q. P. Ha, S. Metia, and M. D. Phung, “Sensing data fusion for enhanced indoor air quality monitoring,” IEEE Sensors Journal, vol. 20, no. 8, pp. 4430–4441, Jan. 2020, doi: https://doi.org/10.1109/JSEN.2020.2964396
Z. Idrees, Z. Zou, and L. Zheng, “Edge computing based IoT architecture for low-cost air pollution monitoring systems: A comprehensive system analysis, design considerations & development,” Sensors, vol. 18, no. 9, p. 3021, Sep. 2018, doi: https://doi.org/10.3390/s18093021
Y. Cheng, X. Li, Z. Li, S. Jiang, Y. Li, J. Jia, and X. Jiang, “AirCloud: A cloud-based air-quality monitoring system for everyone,” in Proc. 12th ACM Conf. Embedded Network Sensor Syst., Nov. 2014, pp. 251–265, doi: https://doi.org/10.1145/2668332.2668346
Y. Liu, J. Nie, X. Li, S. H. Ahmed, W. Y. Lim, and C. Miao, “Federated learning in the sky: Aerial-ground air quality sensing framework with UAV swarms,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9827–9837, Sep. 2020, doi: https://doi.org/10.1109/JIOT.2020.3021006
M. Sunitha, T. Adilakshmi, and M. L. Prasanna, “Vayu – An IoT powered indoor air quality monitoring system,” Journal of Information Systems Engineering and Management, vol. 10, no. 34s, pp. 1059–1072, 2025. doi: https://doi.org/10.52783/jisem.v10i34s.5910
S. Abimannan, E. S. El-Alfy, S. Hussain, Y. S. Chang, S. Shukla, D. Satheesh, and J. G. Breslin, “Towards federated learning and multi-access edge computing for air quality monitoring: Literature review and assessment,” Sustainability, vol. 15, no. 18, p. 13951, Sep. 2023, doi: https://doi.org/10.3390/su151813951
K. T. Putra, H. C. Chen, Prayitno, M. R. Ogiela, C. L. Chou, C. E. Weng, and Z. Y. Shae, “Federated compressed learning edge computing framework with ensuring data privacy for PM2.5 prediction in smart city sensing applications,” Sensors, vol. 21, no. 13, p. 4586, Jul. 2021, doi: https://doi.org/10.3390/s21134586
S. Chadalavada, O. Faust, M. Salvi, S. Seoni, N. Raj, U. Raghavendra, A. Gudigar, P. D. Barua, F. Molinari, and R. Acharya, “Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review,” Environmental Modelling & Software, vol. 185, p. 106312, Feb. 2025, doi: https://doi.org/10.1016/j.envsoft.2024.106312
I. Gryech, C. Asaad, M. Ghogho, and A. Kobbane, “Applications of machine learning and Internet of Things for outdoor air pollution monitoring and prediction: A systematic literature review,” Eng. Appl. Artif. Intell., vol. 137, p. 109182, Nov. 2024, doi: https://doi.org/10.1016/j.engappai.2024.109182
