Weather Variability Control in Three Colombian Airports

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 208)


The aeronautic sector has been economically affected by the closure of its operations with the appearance of the Covid-19. For reducing the impact of weather variables at airport operations, we present a predictive model for better planning. Better planning reduces operative costs and increase the level of client satisfaction. This paper uses hourly observation from 2011 to 2018 at three Colombian airports: The Dorado airport in Bogota, the Olaya Herrera airport in Medellin, and the Matecana airport in Pereira. We build prediction models with deep learning and machine learning methods. These models aim to forecast horizontal and vertical visibility variables with minimum errors. The Random Forest decision tree model performs better predicting theses variables in one, six, and twenty-four hours. This model has better results with the horizontal variable visibility forecasting for the three airports giving errors among 4% and 8%. This algorithm gave a flexible solution, and any airport can implement it.


Random forest Horizontal visibility Vertical visibility 


  1. 1.
    Semana: ¿En qué consiste la ley de quiebras a la que se sometió Avianca en EE. UU.?. Rev. Sem. (2020)Google Scholar
  2. 2.
    Aerocivil: En 9,1 por ciento aumentó el tráfico de pasajeros movilizados vía aérea en 2019, Grup. Counicación y Prensa - Unidad Adm. Espec. Aeronáutica Civ. vol. 2019, pp. 2019–2021 (2020)Google Scholar
  3. 3.
    Dietz, S.J., Kneringer, P., Mayr, G.J., Zeileis, A.: Correction to: forecasting low-visibility procedure states with tree-based statistical methods (Pure Appl. Geophys. 176(6), 2631–2644 (2019))., Pure Appl. Geophys. 176(6), 2645–2658 (2019).
  4. 4.
    Herman, G.R., Schumacher, R.S.: Using reforecasts to improve forecasting of fog and visibility for aviation. Weather Forecast. 31(2), 467–482 (2016). Scholar
  5. 5.
    Zhu, L., Zhu, G., Han, L., Wang, N.: The application of deep learning in airport visibility forecast. Atmos. Clim. Sci. 07(03), 314–322 (2017). Scholar
  6. 6.
    ISU Department of Agronomy: Iowa Enviromental Mesonet (2020).
  7. 7.
    Medina-Merino, R.F., Ñique-Chacón, C.I.: Bosques aleatorios como extensión de los árboles de clasificación con los programas R y Python. Interfases (010), 165 (2017).
  8. 8.
    Neira-Rodado, D., Nugent, C., Cleland, I., Velasquez, J., Viloria, A.: Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition. Sensors (Switzerland) 20(7) (2020).
  9. 9.
    Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues 9(5), 272–278 (2012)Google Scholar
  10. 10.
    Vargas, K., Gonzalez, A., Silva, J.: The Effect of Global Political Risk on Stock Returns: A Cross-Sectional and a Time-Series Analysis BT - Intelligent Computing, Information and Control Systems, pp. 540–548 (2020)Google Scholar
  11. 11.
    Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7(3), 1247–1250 (2014). Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Universidad de La CostaBarranquillaColombia

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