Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks
This paper focuses on improving the prediction of the daily concentration of the pollutants, PM<sub>10</sub> and nitrogen oxides (NO, NO<sub>2</sub>) in the air at urban monitoring sites using 1D convolutional neural networks (CNN). The results show that the 1D CNN model outp...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/68/1/16 |
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| Summary: | This paper focuses on improving the prediction of the daily concentration of the pollutants, PM<sub>10</sub> and nitrogen oxides (NO, NO<sub>2</sub>) in the air at urban monitoring sites using 1D convolutional neural networks (CNN). The results show that the 1D CNN model outperforms the other machine learning models (LSTM and Random Forest) in terms of the coefficients of determination and absolute errors. |
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| ISSN: | 2673-4591 |