Machine Learning algorithms for air pollutants forecasting

被引：0

作者：

Dobrea, Marius ^{[1
]}

Badicu, Andreea ^{[1
]}

Barbu, Marina ^{[1
]}

Subea, Oana ^{[1
]}

Balanescu, Mihaela ^{[1
]}

Suciu, Geroge ^{[1
]}

Birdici, Andrei ^{[1
]}

Orza, Oana ^{[1
]}

Dobre, Ciprian ^{[2
]}

机构：

[1] BEIA Consult Int, Res & Dev Dept, Bucharest, Romania

[2] Univ POLITEHNICA, Dept Comp, Bucharest, Romania

来源：

2020 IEEE 26TH INTERNATIONAL SYMPOSIUM FOR DESIGN AND TECHNOLOGY IN ELECTRONIC PACKAGING (SIITME 2020) | 2020年

关键词：

Machine Learning; Air Pollution; Forecasting; Time Series;

D O I：

10.1109/siitme50350.2020.9292238

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Air pollution represents an issue that raises many concerns nowadays, as it has various negative effects on the environment and the economy worldwide. Because of the rapid urbanization, cities are suffering from polluted air, so it is important to predict future air quality. For this purpose, new applications of artificial intelligence should be employed. In this paper, we will present several Machine Learning algorithms, the possible software that can be used for them and the applications used in the field of air quality. Based on the research in the field, we propose SVR, ARIMA and LSTM, 3 Machine Learning models, which can be used to predict air pollution. These algorithms have been tested using time-series for PM10 and PM2.5 particles. The results showed that SVR and ARIMA algorithms are the most suitable in forecasting air pollutant concentrations.

引用

页码：109 / 113

页数：5

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