Comparative assessment of modeling deep learning networks for modeling ground-level ozone concentrations of pandemic lock-down period
© 2021 Elsevier B.V. All rights reserved.
| Veröffentlicht in: | Ecological modelling. - 1980. - 457(2021) vom: 01. Okt., Seite 109676 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | Ecological modelling |
| Schlagworte: | Journal Article COVID-19 Deep learning Ground-level ozone Long short term memory (LSTM) Pandemic lock-down |
| Zusammenfassung: | © 2021 Elsevier B.V. All rights reserved. Covid-19 pandemic lock-down has resulted significant differences in air quality levels all over the world. In contrary to decrease seen in primary pollutant species, many of the countries have experienced elevated ground-level ozone levels in this period. Air pollution forecast gains more importance to achieve air quality management and take measures against the risks under such extra-ordinary conditions. Statistical models are indispensable tools for predicting air pollution levels. Considering the complex photochemical reactions involved in tropospheric ozone formation, modeling this pollutant requires efficient non-linear approaches. In this study, deep learning methods were applied to forecast hourly ozone levels during pandemic lock-down for an industrialized region in Turkey. With this aim, different deep learning methods were tested and efficiencies of the models were compared considering the calculated RMSE, MAE, R 2 and loss values |
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| Beschreibung: | Date Revised 27.12.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0304-3800 |
| DOI: | 10.1016/j.ecolmodel.2021.109676 |