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Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan

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Abstract

Air pollution is one of the most serious hazards to humans' health nowadays, it is an invisible killer that takes many human lives every year. There are many pollutants existing in the atmosphere today, ozone being one of the most threatening pollutants. It can cause serious health damage such as wheezing, asthma, inflammation, and early mortality rates. Although air pollution could be forecasted using chemical and physical models, machine learning techniques showed promising results in this area, especially artificial neural networks. Despite its importance, there has not been any research on predicting ground-level ozone in Jordan. In this paper, we build a model for predicting ozone concentration for the next day in Amman, Jordan using a mixture of meteorological and seasonal variables of the previous day. We compare a multi-layer perceptron neural network (MLP), support vector regression (SVR), decision tree regression (DTR), and extreme gradient boosting (XGBoost) algorithms. We also explore the effect of applying various smoothing filters on the time-series data such as moving average, Holt-Winters smoothing and Savitzky-Golay filters. We find that MLP outperformed the other algorithms and that using Savitzky-Golay improved the results by 50% for coefficient of determination (R2) and 80% for root mean square error (RMSE) and mean absolute error (MAE). Another point we focus on is the variables required to predict ozone concentration. In order to reduce the time required for prediction, we perform feature selection which greatly reduces the time by 91% as well as shrinking the number of features required for prediction to the previous day values of ozone, humidity, and temperature. The final model scored 98.653% for R2, 1.016 ppb for RMSE and 0.800 ppb for MAE.

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Acknowledgements

The authors are grateful to the Applied Science Private University, Amman, Jordan, for the financial support granted to this research. The authors would also like to thank the Jordanian Ministry of Environment for giving them access to perform scientific research on the King Al-Hussein Public Parks air pollution and meteorological data collected by the ministry.

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Authors and Affiliations

  1. Faculty of Information Technology, Applied Science Private University, Amman, 11931, Jordan

    Maryam Aljanabi, Mohammad Shkoukani & Mohammad Hijjawi

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  1. Maryam Aljanabi
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  2. Mohammad Shkoukani
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  3. Mohammad Hijjawi
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Corresponding author

Correspondence to Mohammad Shkoukani.

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Recommended by Associate Editor Paul Stewart

Maryam Aljanabi received the B. Eng. degree in computer engineering from Omar Almukhtar University, Libya in 2017, and the M. Sc. degree in computer science from the Applied Science Private University, Jordan in 2020.

Her research interests include machine learning and its applications, artificial intelligence, environmental science, and data science.

Mohammad Shkoukani received the B. Sc. degree from Applied Science Private University, Jordan in 2002, and M. Sc. degree from Arab Academy for Banking and Financial Sciences, Jordan in 2004, both in computer. He received the Ph. D. degree in computer information systems from Arab Academy for Banking and Financial Sciences, Jordan in 2009. He is an associate professor at Applied Science Private University, Jordan.

His research interests include agent oriented software engineering, information systems security, and machine learning.

Mohammad Hijjawi received the Ph. D. degree from Manchester Metropolitan University, UK in 2011. He is an associate professor in Computer Science Department, Faculty of Information Technology, Applied Science Private University, Jordan. He has previous computing based training in several domains. He also acts as the Faculty of Information Technology Dean at Applied Science Private University, Jordan in 2015.

His research interests include natural language processing and machine learning.

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Aljanabi, M., Shkoukani, M. & Hijjawi, M. Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan. Int. J. Autom. Comput. 17, 667–677 (2020). https://doi.org/10.1007/s11633-020-1233-4

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