| dc.contributor.author | Toutouh, Jamal | |
| dc.contributor.author | Lebrusan, Irene | |
| dc.contributor.author | Nesmachnow, Sergio | |
| dc.date.accessioned | 2020-04-14T12:54:12Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | Toutouh J., Lebrusán I., Nesmachnow S. (2020) Computational Intelligence for Evaluating the Air Quality in the Center of Madrid, Spain. In: Dorronsoro B., Ruiz P., de la Torre J., Urda D., Talbi EG. (eds) Optimization and Learning. OLA 2020. Communications in Computer and Information Science, vol 1173. Springer, Cham | en_US |
| dc.identifier.isbn | 9783030419127 | en_US |
| dc.identifier.isbn | 9783030419134 | en_US |
| dc.identifier.issn | 1865-0929 | en_US |
| dc.identifier.issn | 1865-0937 | en_US |
| dc.identifier.uri | http://nrs.harvard.edu/urn-3:HUL.InstRepos:42659253 | * |
| dc.description.abstract | This article presents the application of data analysis and computational intelligence techniques for evaluating the air quality in the center of Madrid, Spain. Polynomial regression and deep learning methods to analyze the time series of nitrogen dioxide concentration, in order to evaluate the effectiveness of Madrid Central, a set of road traffic limitation measures applied in downtown Madrid. According to the reported results, Madrid Central was able to significantly reduce the nitrogen dioxide concentration, thus effectively improving air quality. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer International Publishing | en_US |
| dash.license | IOAL | |
| dc.title | Computational Intelligence for Evaluating the Air Quality in the Center of Madrid, Spain | en_US |
| dc.type | Monograph or Book | en_US |
| dc.description.version | Accepted Manuscript | en_US |
| dc.date.available | 2020-04-14T12:54:12Z | |
| dc.identifier.doi | 10.1007/978-3-030-41913-4_10 | |
| dash.source.page | 115-127 | |
| dash.contributor.affiliated | Lebrusan, Irene | |
