Abstract
European and Spanish legislation set hourly limits for Nitrogen Dioxide, \(NO_2\), that are enforced with traffic restrictions. In this context it is important to warn the citizens in advance, which can only be done if the \(NO_2\) levels are forecasted. In this paper we propose a deep learning based air quality forecasting system that uses air quality and meteorological data to produce \(NO_2\) forecasts up to 24 h with a root mean squared error, RMSE, of 10.54 \(\upmu {\text {g}}/{\text {m}}^3\). We also compare our results with the model based system CALIOPE.
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As of 05/03/2017 we have not received any information regarding the error rate of the system.
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Acknowledgments
This work was partially funded by Banco de Santander and Universidad Rey Juan Carlos in the Funding Program for Excellence Research Groups ref. “Computer Vision and Image Processing (CVIP)”. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. We would also like to thank the Barcelona Supercomputing Center and the Madrid city council for their support during our research.
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Pardo, E., Malpica, N. (2017). Air Quality Forecasting in Madrid Using Long Short-Term Memory Networks. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_24
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DOI: https://doi.org/10.1007/978-3-319-59773-7_24
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