Publicado

2019-04-01

Red Neuronal Artificial aplicado para el pronóstico de eventos críticos de PM2.5 en el Valle de Aburra.

Artificial neural network applied for the forecast of critical PM2.5 events in the Aburra Valley

DOI:

https://doi.org/10.15446/dyna.v86n209.63228

Palabras clave:

Contaminación atmosférica, Pronóstico de PM2.5, Red Neuronal Artificial, Datos Meteorológicos (es)

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Las grandes implicaciones que tiene en la salud humana la exposición a eventos de contaminación atmosférica, puede tener repercusiones en la calidad de vida, economía, y en la calidad de los ecosistemas de una ciudad. Con la posibilidad de prever un evento crítico, se habilita la opción de tomar medidas adecuadas para la mitigación o incluso la prevención dichos impactos. En este documento, se ha desarrollado y se ha probado un modelo de Redes Neuronales Artificiales (RNA) para pronosticar la concentración diaria del material particulado menor a 2.5 micras (PM2.5) en el Valle de Aburrá (Colombia), con un día de anticipación. Esto, a  partir de información de tres estaciones de la Red de Monitoreo de Calidad del Aire del Área Metropolitana.

The great human health implications of exposure to atmospheric pollution events can have repercussions on the quality of life, economy,
and the quality of city’s ecosystems. With the possibility of predicting a critical event, the option of taking adequate measures for mitigation
or even prevention of these impacts is enabled. In this paper, an Artificial Neural Networks (RNA) model was developed and tested to
predict the daily concentration of particulate matter less than 2.5 microns (PM2.5) in the Aburrá Valley (Colombia), with a day of
anticipation, based on information from three stations of the Metropolitan Area Air Quality Monitoring Network.

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Cómo citar

IEEE

[1]
D. Baena Salazar, J. F. Jiménez, C. E. Zapata, y Álvaro Ramírez-Cardona, «5 en el Valle de Aburra»., DYNA, vol. 86, n.º 209, pp. 347–356, abr. 2019.

ACM

[1]
Baena Salazar, D., Jiménez, J.F., Zapata, C.E. y Ramírez-Cardona, Álvaro 2019. Red Neuronal Artificial aplicado para el pronóstico de eventos críticos de PM2.5 en el Valle de Aburra. DYNA. 86, 209 (abr. 2019), 347–356. DOI:https://doi.org/10.15446/dyna.v86n209.63228.

ACS

(1)
Baena Salazar, D.; Jiménez, J. F.; Zapata, C. E.; Ramírez-Cardona, Álvaro. Red Neuronal Artificial aplicado para el pronóstico de eventos críticos de PM2.5 en el Valle de Aburra. DYNA 2019, 86, 347-356.

APA

Baena Salazar, D., Jiménez, J. F., Zapata, C. E. & Ramírez-Cardona, Álvaro. (2019). Red Neuronal Artificial aplicado para el pronóstico de eventos críticos de PM2.5 en el Valle de Aburra. DYNA, 86(209), 347–356. https://doi.org/10.15446/dyna.v86n209.63228

ABNT

BAENA SALAZAR, D.; JIMÉNEZ, J. F.; ZAPATA, C. E.; RAMÍREZ-CARDONA, Álvaro. Red Neuronal Artificial aplicado para el pronóstico de eventos críticos de PM2.5 en el Valle de Aburra. DYNA, [S. l.], v. 86, n. 209, p. 347–356, 2019. DOI: 10.15446/dyna.v86n209.63228. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/63228. Acesso em: 14 mar. 2026.

Chicago

Baena Salazar, Daniela, José F. Jiménez, Carmen E. Zapata, y Álvaro Ramírez-Cardona. 2019. «5 en el Valle de Aburra». DYNA 86 (209):347-56. https://doi.org/10.15446/dyna.v86n209.63228.

Harvard

Baena Salazar, D., Jiménez, J. F., Zapata, C. E. y Ramírez-Cardona, Álvaro (2019) «5 en el Valle de Aburra»., DYNA, 86(209), pp. 347–356. doi: 10.15446/dyna.v86n209.63228.

MLA

Baena Salazar, D., J. F. Jiménez, C. E. Zapata, y Álvaro Ramírez-Cardona. «5 en el Valle de Aburra». DYNA, vol. 86, n.º 209, abril de 2019, pp. 347-56, doi:10.15446/dyna.v86n209.63228.

Turabian

Baena Salazar, Daniela, José F. Jiménez, Carmen E. Zapata, y Álvaro Ramírez-Cardona. «5 en el Valle de Aburra». DYNA 86, no. 209 (abril 1, 2019): 347–356. Accedido marzo 14, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/63228.

Vancouver

1.
Baena Salazar D, Jiménez JF, Zapata CE, Ramírez-Cardona Álvaro. Red Neuronal Artificial aplicado para el pronóstico de eventos críticos de PM2.5 en el Valle de Aburra. DYNA [Internet]. 1 de abril de 2019 [citado 14 de marzo de 2026];86(209):347-56. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/63228

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CrossRef citations4

1. Caroline Mogollón-Sotelo, Alejandro Casallas, Sergio Vidal, Nathalia Celis, Camilo Ferro, Luis Belalcazar. (2021). A support vector machine model to forecast ground-level PM2.5 in a highly populated city with a complex terrain. Air Quality, Atmosphere & Health, 14(3), p.399. https://doi.org/10.1007/s11869-020-00945-0.

2. Paola Andrea Sánchez-Sánchez, José Rafael García-González, Leidy Haidy Perez Coronell. (2020). Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making. Advances in Business Strategy and Competitive Advantage. , p.118. https://doi.org/10.4018/978-1-7998-3351-2.ch007.

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