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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-07-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/68/1/16 |
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| author | Ivan Gudelj Mario Lovrić Emmanuel Karlo Nyarko |
| author_facet | Ivan Gudelj Mario Lovrić Emmanuel Karlo Nyarko |
| author_sort | Ivan Gudelj |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5f3ed0112e084d37bb49578936759ad3 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-5f3ed0112e084d37bb49578936759ad32025-08-20T02:11:17ZengMDPI AGEngineering Proceedings2673-45912024-07-016811610.3390/engproc2024068016Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural NetworksIvan Gudelj0Mario Lovrić1Emmanuel Karlo Nyarko2Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, CroatiaCentre for Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, CroatiaFaculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, CroatiaThis 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.https://www.mdpi.com/2673-4591/68/1/16air pollutionmachine learning1D CNN |
| spellingShingle | Ivan Gudelj Mario Lovrić Emmanuel Karlo Nyarko Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks Engineering Proceedings air pollution machine learning 1D CNN |
| title | Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks |
| title_full | Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks |
| title_fullStr | Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks |
| title_full_unstemmed | Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks |
| title_short | Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks |
| title_sort | modelling the daily concentration of airborne particles using 1d convolutional neural networks |
| topic | air pollution machine learning 1D CNN |
| url | https://www.mdpi.com/2673-4591/68/1/16 |
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