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dc.creatorVargas-Daza, Karen
dc.creatorMisat, Giovanny
dc.creatorNeira Rodado, Dionicio
dc.description.abstractThe 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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.sourcemart Innovation, Systems and Technologiesspa
dc.subjectRandom forestspa
dc.subjectHorizontal visibilityspa
dc.subjectVertical visibilityspa
dc.titleWeather variability control in three Colombian airportsspa
dcterms.references1. Semana: ¿En qué consiste la ley de quiebras a la que se sometió Avianca en EE. UU.?. Rev. Sem. (2020)spa
dcterms.references2. 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)spa
dcterms.references3. 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).
dcterms.references4. Herman, G.R., Schumacher, R.S.: Using reforecasts to improve forecasting of fog and visibility for aviation. Weather Forecast. 31(2), 467–482 (2016).spa
dcterms.references5. 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).spa
dcterms.references6. ISU Department of Agronomy: Iowa Enviromental Mesonet (2020).spa
dcterms.references7. 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).spa
dcterms.references8. 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).spa
dcterms.references9. Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues 9(5), 272–278 (2012)spa
dcterms.references10. 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)spa
dcterms.references11. 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).spa

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Attribution-NonCommercial-NoDerivatives 4.0 International
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