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.
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Acknowledgements
I. Lebrusán has been partially funded by RCC Harvard program. J. Toutouh has been partially funded by EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 799078; and by the Spanish MINECO and FEDER projects TIN2017-88213-R, RTI2018-100754-B-I00, and UMA18-FEDERJA-003.
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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. https://doi.org/10.1007/978-3-030-41913-4_10
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