Abstract
Air pollution is considered one of the biggest health threats after it has become the fourth leading cause of death in the world. According to the Health Effect Institute (HEI), 95% of the world’s population is currently breathing polluted air. This paper highlights the importance of using machine learning algorithms to classify and predict air pollution based on collected real-time environmental data. These algorithms would help decision makers and responsible authorities to take action to alleviate this critical situation. Machine learning algorithms will be evaluated with offline data and real-time data which will be collected through pollution sensors as a model study. The obtained results revealed that Artificial Neural Network had the best performance and the highest accuracy among KNN, SVM, and Naïve Bayes Classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chabarekh, C., Afif, C., Nagl, C., Mitri, G.: Lebanon’s National Strategy for Air Quality Management for 2030, Department of Air Quality, Service of Environmental Technology, MoE (2017)
Bishoi, B., Prakash, A., Jain, V.K.: A comparative study of air quality index based on factor analysis and US-EPA methods for an urban environment. Aerosol Air Qual. Res. 9, 1–17 (2009)
Sanjeevi, M.: Different types of machine learning and their types. Medium, 26 September 2017. https://medium.com/deep-math-machine-learning-ai/different-types-of-machine-learning-and-their-types-34760b9128a2
National Oceanic and Atmospheric Administration (NOAA): State of the science fact sheet, United States Department of Commerce, Washington, DC, Technical report (2016). https://nrc.noaa.gov/CouncilProducts/ScienceFactSheets.aspx
Ayele, T.W., Mehta, R.: Air pollution monitoring and prediction using IoT. In: International Conference on Inventive Communication and Computational Technologies (ICICCT 2018), Vadodara, India (2018)
Ghoneim, O.A., Doreswamy, Manjunatha, B.R.: Forecasting of ozone concentration in smart city using deep learning. Department of Computer Science, Mangalore University, Karnataka, India (2016)
Mbarak, A., Yetis, Y., Jamshidi, M.: Data-based pollution forecasting via machine: case of Northwest Texas. Department of Electrical and Computer Engineering University of Texas at San Antonio, San Antonio, Texas, USA
Ong, B.T., Sugiura, K., Zettsu, K.: Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5. Neural Comput. Appl. 27, 1553–1566 (2016). https://doi.org/10.1007/s00521-015-1955-3
Yi, X., Zhang, J., Wang, Z., Li, T., Zheng, Y.: Deep distributed fusion network for air quality prediction. In: Proceedings of the 24th SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 965–973 (2018)
Wang, Z., Qi, T., Song, G., Hu, W., Li, X., Zhang, Z.M.: Deep air learning: interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Trans. Knowl. Data Eng. 30, 2285–2297 (2018)
T. U. o. W. (NZ). https://www.cs.waikato.ac.nz/ml/WEKA/
Apache Friends. https://www.apachefriends.org
Australia State of the Environment: Air quality index Abient air quality (2016). https://soe.environment.gov.au/theme/ambient-air-quality/topic/2016/air-quality-index
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tlais, S., HajjHussein, H., Sakr, F., Hallani, M., Ahmad, AM., El-Bazzal, Z. (2020). Air Quality Monitoring and Classification Using Machine Learning. In: Thampi, S., Trajkovic, L., Li, KC., Das, S., Wozniak, M., Berretti, S. (eds) Machine Learning and Metaheuristics Algorithms, and Applications. SoMMA 2019. Communications in Computer and Information Science, vol 1203. Springer, Singapore. https://doi.org/10.1007/978-981-15-4301-2_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-4301-2_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4300-5
Online ISBN: 978-981-15-4301-2
eBook Packages: Computer ScienceComputer Science (R0)