Machine Learning algorithms for air pollutants forecasting
| Autor: | Geroge Suciu, Marina Barbu, Marius Dobrea, Mihaela Balanescu, Andrei Birdici, Andreea Badicu, Oana Subea, Ciprian Dobre, Oana Orza |
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| Rok vydání: | 2020 |
| Předmět: |
Air pollutant concentrations
Computer science business.industry Air pollution 02 engineering and technology Atmospheric model 010501 environmental sciences medicine.disease_cause Machine learning computer.software_genre 01 natural sciences Field (computer science) Data modeling 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Applications of artificial intelligence Artificial intelligence Autoregressive integrated moving average business Algorithm computer Air quality index 0105 earth and related environmental sciences |
| Zdroj: | 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME). |
| DOI: | 10.1109/siitme50350.2020.9292238 |
| Popis: | 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 PM 10 and PM 2.5 particles. The results showed that SVR and ARIMA algorithms are the most suitable in forecasting air pollutant concentrations. |
| Databáze: | OpenAIRE |
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