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

Ekinci E., İLHAN OMURCA S., ÖZBAY B.

ECOLOGICAL MODELLING, cilt.457, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 457
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.ecolmodel.2021.109676
  • Dergi Adı: ECOLOGICAL MODELLING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Communication Abstracts, Environment Index, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Ground-level ozone, Pandemic lock-down, COVID-19, Deep learning, Long short term memory (LSTM), TROPOSPHERIC OZONE, NEURAL-NETWORK, PREDICTION, REGRESSION
  • Kocaeli Üniversitesi Adresli: Evet

Özet

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.