Big Data AI System for Air Quality Prediction

Main Article Content

Roba Zayed
Maysam Abbod

Abstract

Air Quality has been a research field for many investigators from varied disciplines in respect to global heating, climate change, health effect theories and others. Predicting air quality status is becoming more complex with time due to different air gases and other components. This paper aims at presenting machine learning models and techniques to predict air quality levels in cities providing accuracy measures to support data driven decision making in various sectors aligned with sustainable development, economic growth and social values. The research supports air quality policies formulation with a forward looking to eliminate global related consequences, save the world from the dangerous earth pollution and to close the gap in air quality index standardization with emphasis on cities sustainable development.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
R. Zayed and M. Abbod, “Big Data AI System for Air Quality Prediction”, DataSCI, vol. 4, no. 2, pp. 5-10, Jan. 2022.
Section
Research Articles

References

Rybarczyk, Y. and Zalakeviciute, R. (2018) ‘Machine learning Approaches for outdoor air quality modelling: A systematic review’, Applied Sciences. MDPI AG, 8(12), p. 2570. doi: 10.3390/app8122570.

Alkasassbeh, M et al. (2013) ‘Prediction of PM10 and TSP Air Pollution Parameters Using Artificial Neural Network Autoregressive, External Input Models: A Case Study in Salt, Jordan’, Middle-East Journal of Scientific Research, 14(7), pp. 999–1009. doi: 10.5829/idosi.mejsr.2013.14.7.2171.

Zhang, J., & Ding, W. (2017). Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong. International Journal of Environmental Research and Public Health, 14(2), 114. https://doi.org/10.3390/ijerph14020114

Rao, P. (2014) A survey on Air Quality forecasting Techniques. Available at: www.ijcsit.com (Accessed: 1 February 2020).

Gao, J. (2018) ‘Air Quality Prediction: Big Data and Machine Learning Approaches’. doi: 10.18178/ijesd.2018.9.1.1066.

Chapman, L. (2007). Transport and climate change: a review. Journal of Transport Geography, 15(5), 354–367. https://doi.org/10.1016/j.jtrangeo.2006.11.008

Grivas, G. and Chaloulakou, A. (2006) ‘Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece’, Atmospheric Environment. Pergamon, 40(7), pp. 1216–1229. doi: 10.1016/j.atmosenv.2005.10.036.

Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., & Li, T. (2015). Forecasting Fine-Grained Air Quality Based on Big Data. https://doi.org/10.1145/2783258.2788573

dos.gov.jo (Accessing date: 21 July 2021)

https://www.londonair.org.uk/LondonAir/Default.aspx

Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., & Wang, Y. S. (2019). Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. Journal of Cleaner Production, 209, 134–145. https://doi.org/10.1016/j.jclepro.2018.10.243

Baldasano, J. M., Valera, E., & Jiménez, P. (2003). Air quality data from large cities. Science of the Total Environment, 307(1–3), 141–165. https://doi.org/10.1016/S0048-9697(02)00537-5.