Deep learning-based long-term prediction of air quality parameters
| dc.contributor.author | Gökçek, Öznur Begüm | |
| dc.contributor.author | Dokuz, Yeşim | |
| dc.contributor.author | Bozdağ, Aslı | |
| dc.date.accessioned | 2024-11-07T10:40:04Z | |
| dc.date.available | 2024-11-07T10:40:04Z | |
| dc.date.issued | 2021 | |
| dc.department | Niğde Ömer Halisdemir Üniversitesi | |
| dc.description.abstract | 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. | |
| dc.identifier.doi | 10.1007/s12517-021-08628-5 | |
| dc.identifier.issn | 1866-7511 | |
| dc.identifier.issue | 21 | |
| dc.identifier.scopus | 2-s2.0-85118347711 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://doi.org/10.1007/s12517-021-08628-5 | |
| dc.identifier.uri | https://hdl.handle.net/11480/11404 | |
| dc.identifier.volume | 14 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.relation.ispartof | Arabian Journal of Geosciences | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241106 | |
| dc.subject | Air pollutant | |
| dc.subject | NO | |
| dc.subject | NO<sub>2</sub> | |
| dc.subject | NO<sub>x</sub> | |
| dc.subject | PM<sub>10</sub> | |
| dc.subject | SO<sub>2</sub> | |
| dc.subject | Time series LSTM | |
| dc.title | Deep learning-based long-term prediction of air quality parameters | |
| dc.type | Article |
