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Comparative assessment of modeling deep learning networks for modeling ground-level ozone concentrations of pandemic lock-down period

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  • Ekinci, Ekin
  • İlhan Omurca, Sevinç
  • Özbay, Bilge

Abstract

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, R2 and loss values.

Suggested Citation

  • Ekinci, Ekin & İlhan Omurca, Sevinç & Özbay, Bilge, 2021. "Comparative assessment of modeling deep learning networks for modeling ground-level ozone concentrations of pandemic lock-down period," Ecological Modelling, Elsevier, vol. 457(C).
    • Handle: RePEc:eee:ecomod:v:457:y:2021:i:c:s0304380021002349
    DOI: 10.1016/j.ecolmodel.2021.109676
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    References listed on IDEAS

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    1. Christos Agiakloglou & Apostolos Tsimpanos, 2021. "Evaluating information criteria for selecting spatial processes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(3), pages 677-697, June.
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    Cited by:

    1. Sichen Wang & Xi Mu & Peng Jiang & Yanfeng Huo & Li Zhu & Zhiqiang Zhu & Yanlan Wu, 2022. "New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China," IJERPH, MDPI, vol. 19(12), pages 1-15, June.

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