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Boundary-Layer Meteorology

, Volume 165, Issue 2, pp 349–370 | Cite as

Efficient Prediction of Low-Visibility Events at Airports Using Machine-Learning Regression

  • L. Cornejo-Bueno
  • C. Casanova-Mateo
  • J. Sanz-Justo
  • E. Cerro-Prada
  • S. Salcedo-Sanz
Research Article

Abstract

We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is \({>}\)1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions (\({<}\)500 m). However, we show improved results of all the methods when data from a neighbouring meteorological tower are included, and also with a pre-processing scheme using a wavelet transform. Also presented are results of the algorithm performance in daytime and nighttime conditions, and for different prediction time horizons.

Keywords

Airports Algorithms Fog prediction Low-visibility events Machine learning 

Notes

Acknowledgements

This work has been partially supported by Comunidad de Madrid, under the Project No. S2013/ICE-2933, and by Project TIN2014-54583-C2-2-R of the Spanish Ministerial Commission of Science and Technology (MICYT).

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • L. Cornejo-Bueno
    • 1
  • C. Casanova-Mateo
    • 2
    • 3
  • J. Sanz-Justo
    • 2
  • E. Cerro-Prada
    • 3
  • S. Salcedo-Sanz
    • 1
  1. 1.Department of Signal Processing and CommunicationsUniversidad de AlcaláAlcalá de HenaresSpain
  2. 2.LATUV Remote Sensing LaboratoryUniversidad de ValladolidValladolidSpain
  3. 3.Department of Civil Engineering: Construction, Infrastructure and TransportUniversidad Politécnica de MadridMadridSpain

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