Deep learning-based long-term prediction of air quality parameters
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, PM10, SO2, NO2, NO, and NOX concentration values obtained for 2012–2018 from 7 different locations in Ankara city in Turkey were trained with deep learning systems, and predictions for the future were made. To make future predictions, time-based long short-term memory (LSTM) deep learning model was used. With the help of this model, it was predicted which values the air quality parameters determined in the city of Ankara for 2018 would take, and they were compared with the actual values of the same year. Accordingly, PM10 (R2 = 0.95, RMSE = 7.94), SO2 (R2 = 0.99, RMSE = 0.35), NO (R2 = 0.98, RMSE = 5.03), NO2 (R2 = 0.98, RMSE = 2.32), and NOX (R2 = 0.98, RMSE = 6.86) at almost all locations exhibited quite high performance for LSTM. According to the performance criteria obtained, it can be said that LSTM is useful in predicting air quality parameters and works successfully. © 2021, Saudi Society for Geosciences.
Açıklama
Anahtar Kelimeler
Air pollutant, NO, NO<sub>2</sub>, NO<sub>x</sub>, PM<sub>10</sub>, SO<sub>2</sub>, Time series LSTM
Kaynak
Arabian Journal of Geosciences
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
14
Sayı
21
