Weather variability control in three Colombian airports
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 l...
- Autores:
-
Vargas-Daza, Karen
Misat, Giovanny
Neira Rodado, Dionicio
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7722
- Acceso en línea:
- https://hdl.handle.net/11323/7722
https://doi.org/10.1007/978-981-33-4256-9_37
https://repositorio.cuc.edu.co/
- Palabra clave:
- Random forest
Horizontal visibility
Vertical visibility
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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| dc.title.spa.fl_str_mv |
Weather variability control in three Colombian airports |
| title |
Weather variability control in three Colombian airports |
| spellingShingle |
Weather variability control in three Colombian airports Random forest Horizontal visibility Vertical visibility |
| title_short |
Weather variability control in three Colombian airports |
| title_full |
Weather variability control in three Colombian airports |
| title_fullStr |
Weather variability control in three Colombian airports |
| title_full_unstemmed |
Weather variability control in three Colombian airports |
| title_sort |
Weather variability control in three Colombian airports |
| dc.creator.fl_str_mv |
Vargas-Daza, Karen Misat, Giovanny Neira Rodado, Dionicio |
| dc.contributor.author.spa.fl_str_mv |
Vargas-Daza, Karen Misat, Giovanny Neira Rodado, Dionicio |
| dc.subject.spa.fl_str_mv |
Random forest Horizontal visibility Vertical visibility |
| topic |
Random forest Horizontal visibility Vertical visibility |
| description |
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. |
| publishDate |
2021 |
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2021-01-19T21:23:09Z |
| dc.date.available.none.fl_str_mv |
2021-01-19T21:23:09Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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https://hdl.handle.net/11323/7722 |
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https://doi.org/10.1007/978-981-33-4256-9_37 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/7722 https://doi.org/10.1007/978-981-33-4256-9_37 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
1. Semana: ¿En qué consiste la ley de quiebras a la que se sometió Avianca en EE. UU.?. Rev. Sem. (2020) 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) 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)). https://doi.org/10.1007/s00024-018-1914-x), Pure Appl. Geophys. 176(6), 2645–2658 (2019). https://doi.org/10.1007/s00024-018-1993-8 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). 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). 6. ISU Department of Agronomy: Iowa Enviromental Mesonet (2020). 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. 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. Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues 9(5), 272–278 (2012) 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) 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). |
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Vargas-Daza, Karen972c0049b215ab4cefc970881a7f2832Misat, Giovanny4dc441edc7f41cf86ff4e75a9c36684eNeira Rodado, Dionicio3be1ac9c504a1282ad14d2dee803adcf2021-01-19T21:23:09Z2021-01-19T21:23:09Z2021https://hdl.handle.net/11323/7722https://doi.org/10.1007/978-981-33-4256-9_37Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2mart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-33-4256-9_37Random forestHorizontal visibilityVertical visibilityWeather variability control in three Colombian airportsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Semana: ¿En qué consiste la ley de quiebras a la que se sometió Avianca en EE. UU.?. Rev. Sem. (2020)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)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)). https://doi.org/10.1007/s00024-018-1914-x), Pure Appl. Geophys. 176(6), 2645–2658 (2019). https://doi.org/10.1007/s00024-018-1993-84. Herman, G.R., Schumacher, R.S.: Using reforecasts to improve forecasting of fog and visibility for aviation. Weather Forecast. 31(2), 467–482 (2016).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).6. ISU Department of Agronomy: Iowa Enviromental Mesonet (2020).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. 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. Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues 9(5), 272–278 (2012)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)11. Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geosci. 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