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

doi:10.1007/s11633-020-1233-4

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan
	Maryam Aljanabi; Mohammad Shkoukani; Mohammad Hijjawi
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2020
卷号	17 期号:5 页码:667-677
摘要	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.
关键词	Ozone prediction machine learning neural networks supervised learning regression.
DOI	10.1007/s11633-020-1233-4
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42266
专题	学术期刊_Machine Intelligence Research
作者单位	Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
推荐引用方式 GB/T 7714	Maryam Aljanabi,Mohammad Shkoukani,Mohammad Hijjawi. Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan[J]. International Journal of Automation and Computing,2020,17(5):667-677.
APA	Maryam Aljanabi,Mohammad Shkoukani,&Mohammad Hijjawi.(2020).Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan.International Journal of Automation and Computing,17(5),667-677.
MLA	Maryam Aljanabi,et al."Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan".International Journal of Automation and Computing 17.5(2020):667-677.

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IJAC-2019-12-247.pdf（1159KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览