PM2.5 Concentration Prediction for Air Pollution using Machine Learning Algorithms | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 12, Volume 28, ICEEM2019-Special Issue, 2019, Page 349-354 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.67375 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed Samy Abd El Aziz Moursi   1; Marwa A. Shouman2; Ezz El-din Hemdan2; Nawal El-Fishawy2
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| 1Computer Science and Engineering Department, Faculty of Electronic Engineering in Menouf, Menoufia University | ||||
| 2Computer Science and Engineering Department Faculty of Electronic Engineering Menouf, Egypt | ||||
| Abstract | ||||
| Air pollution is a phenomenon harmful to both human being existence as well as the ecological system. It is caused by the excess of some substances above a particular concentration in the atmosphere. Atmospheric particulate matter (APM) – or PM for short – threatens life because of its tiny size – diameter is up to 10 micrometers. Their danger comes from their ability to penetrate deeper inside the human respiratory system. (PM2.5) particulates are less than 2.5 micrometers and are more hazardous when compared to (PM10) coarse particles–10 micrometers in size. Hence, environmental agencies and governments seek to explore new methods to predict future air pollution. These endeavors mainly focus on mitigating environmental pollution and predicting pollutants concentrations to take enough precautions for citizens protection. This paper presents various machine learning algorithms that predict PM2.5 concentration for the next hour. These algorithms are Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Random Forest, and Extra Trees. Their performance is measured in Root Mean Square Error (RMSE), coefficient of determination R2, and duration in seconds. Extra Trees shows the least RMSE and the highest coefficient of determination R2. | ||||
| Keywords | ||||
| Air pollution forecast; Atmospheric particulate matter; PM2.5; Machine Learning; SVR; LSTM; Random Forest and Extra Trees | ||||
| References | ||||
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