Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning
ilustraciones, diagramas
- Autores:
-
García Millán, Diana Rocío
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86187
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Ozono troposférico
Machine learning
Calidad del aire
Predicción
Precursores
Tropospheric ozone
Machine Learning
Convolutional
Air quality
Contaminación atmosférica
aprendizaje automático
Modelo de simulación
Air pollution
machine learning
Simulation models
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
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UNACIONAL2 |
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Universidad Nacional de Colombia |
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|
| dc.title.spa.fl_str_mv |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning |
| dc.title.translated.eng.fl_str_mv |
Tropospheric ozone prediction in Bogotá: a machine learning approach |
| title |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning |
| spellingShingle |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Ozono troposférico Machine learning Calidad del aire Predicción Precursores Tropospheric ozone Machine Learning Convolutional Air quality Contaminación atmosférica aprendizaje automático Modelo de simulación Air pollution machine learning Simulation models |
| title_short |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning |
| title_full |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning |
| title_fullStr |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning |
| title_full_unstemmed |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning |
| title_sort |
Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning |
| dc.creator.fl_str_mv |
García Millán, Diana Rocío |
| dc.contributor.advisor.spa.fl_str_mv |
Rojas Roa, Néstor Yesid Casallas Garcia, Alejandro |
| dc.contributor.author.spa.fl_str_mv |
García Millán, Diana Rocío |
| dc.contributor.researchgroup.spa.fl_str_mv |
Calidad del Aire |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Ozono troposférico Machine learning Calidad del aire Predicción Precursores Tropospheric ozone Machine Learning Convolutional Air quality Contaminación atmosférica aprendizaje automático Modelo de simulación Air pollution machine learning Simulation models |
| dc.subject.proposal.spa.fl_str_mv |
Ozono troposférico Machine learning Calidad del aire Predicción Precursores |
| dc.subject.proposal.eng.fl_str_mv |
Tropospheric ozone Machine Learning Convolutional Air quality |
| dc.subject.unesco.spa.fl_str_mv |
Contaminación atmosférica aprendizaje automático Modelo de simulación |
| dc.subject.unesco.eng.fl_str_mv |
Air pollution machine learning Simulation models |
| description |
ilustraciones, diagramas |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T18:40:40Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T18:40:40Z |
| dc.date.issued.none.fl_str_mv |
2024-05 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/86187 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/86187 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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Disponible: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-49932004000200007&lng=en&tlng=es Russo, F., Biancardo , S., Formisano , A., & Dell'Acqua, G. (2018). Predicting percent air voids content in compacted bituminous hot mixture specimens by varying the energy laboratory compaction and the bulk density assessment method. Construction and Building Materials. doi: https://doi.org/10.1016/j.conbuildmat.2017.12.174 Sayeed, A., Choi, Y., Eslami, E., Jung, J., Lops, Y., Khan, A., . . . Choi, M.-H. (2021). A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance. Scientific Reports, vol. 806. doi: https://doi.org/10.1016/j.scitotenv.2021.150149 Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A., & Jung, J. (2020). Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance. 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Obtenido de http://www.ideam.gov.co/web/tiempo-y-clima/ozono-troposferico Shewalkar, A., Nyavanandi, D & Ludwig, S. (2019). Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence and Soft Computing Research. https://doi.org/10.2478/jaiscr-2019-0006 Singh, V., Singh, S. & Biswal, A. (2021). Exceedances and trends of particulate matter (PM2.5) in five Indian megacities. Sci. Total Environ. doi: https://doi.org/10.1016/j.scitotenv.2020.141461 Sokhi, R., Singh, V., Querol, X., Finardi, S., Targino, A., Andrade, M., . . . Zavala, M. (2021). A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions,. Environment International. doi: doi.org/10.1016/j.envint.2021.106818 Sundermeyer, M., Schlüter, R. & Ney, H. (2012). LSTM Neural Networks for Language Modeling. 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Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-50974-2_31 University Colles London, Universidad de los Andes. (2013). Caracterización de la contaminación atmosférica en Colombia. Reino Unido - Colombia. Obtenido de https://prosperityfund.uniandes.edu.co/site/wp-content/uploads/Caracterizaci%C3%B3n-de-la-contaminaci%C3%B3n-atmosf%C3%A9rica-en-Colombia.pdf Valencia, R., Sanchez, G., & Díaz I. (2016). A general regression neural network for modeling the behavior of PM10 concentration level in Santa Marta, Colombia. ARPN J Eng Appl Sci, ISSN 1819-6608 Von Schneidemesser, E., Monks, P., Allan, J., Bruhwiler, L., Forster, P., Fowler, D., . . . Sutton, M. (2015). Chemistry and the linkages between air quality and climate change. Chem. Rev. doi: https://doi.org/10.1021/acs.chemrev.5b00089 Wang, J., Wang, D., Ge, B., Lin, W., Ji, D., Pan, X., . . . Wang, Z. (2022). 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Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rojas Roa, Néstor Yesid827f8ba58948271b39915a46415778ed600Casallas Garcia, Alejandro5b1a2d22be723e19c84a3c27911d9661García Millán, Diana Rocío5a7abb64ac386e5f39f2d8745309b5b0Calidad del Aire2024-05-30T18:40:40Z2024-05-30T18:40:40Z2024-05https://repositorio.unal.edu.co/handle/unal/86187Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEl ozono troposférico es una preocupación ambiental que tiene repercusiones tanto en la salud humana como en los ecosistemas, aún más, cuando su producción depende directamente de factores tanto ambientales como antropogénicos. En los últimos años, en Bogotá, el ozono ha tenido una tendencia de aumento en su concentración. Por ello, es imperativo estudiar la razón por la cual este incremento se está presentando, así como realizar avances en modelos que permitan realizar alertas tempranas y reducir el riesgo. La predicción es crucial para concientizar sobre la salud pública y la implementación de estrategias de gestión de la calidad del aire. Se realiza un estudio general del comportamiento y evolución del ozono (O3) en las diferentes zonas de Bogotá. Este proyecto tiene como objetivo diseñar un modelo predictivo de aprendizaje automático (machine learning) de los niveles de O3 en Bogotá utilizando datos de la Red de Monitoreo de Calidad del Aire de Bogotá (RMCAB), incluyendo mediciones de precursores de ozono y variables meteorológicas. Los modelos de aprendizaje automático utilizados fueron redes convolucionales y capas de memoria bireccional a largo plazo (LSTM) de la biblioteca Tensor Flow Keras y el paquete Python Sklearn, para facilitar la categorización de técnicas de inteligencia artificial. Esto da como resultado un modelo que destaca por su capacidad de ofrecer pronósticos altamente precisos al considerar y cuantificar la influencia de los precursores. Los resultados del modelo determinaron una correlación de Spearman mayor a 0.6, la raíz del error cuadrático medio se mantuvo por debajo de 10 µg/m³ en todos los casos y el Índice de Ajuste superó el valor de 0.5 en todos los casos, categorizados como bueno, excelente y bueno respectivamente, lo cual sugiere que el modelo replica con precisión y exactitud el comportamiento y la tendencia del ozono. Se espera que la metodología aplicada sirva como referencia para continuar con la predicción de otros contaminantes atmosféricos de interés y de esta forma dar apoyo a la toma de decisiones que permitan minimizar los impactos ambientales enfocados a la calidad del aire. (Texto tomado de la fuente).Tropospheric ozone is an environmental concern has repercussions on both human health and ecosystems, even more so when its production depends directly on both environmental and anthropogenic factors. In recent years, in Bogotá, ozone has had a trend of increasing concentration. Therefore, it is imperative to study the reason why this increase is occurring, as well as make advances in models that allow early warnings and risk reduction. The prediction is crucial for public health awareness and implementation of air quality management strategies. A general study of the behavior and evolution of ozone (O3) in the different areas of Bogotá is carried out. This project aims to create a predictive machine learning model for ozone levels in Bogotá using data from the air quality monitoring network, including measurements of O3 precursors and meteorological variables. Machine learning models used were Convolutional Networks and Birectional Long Short-Term Memory (LSTM) layers from the Tensor Flow Keras library, and the Python package Sklearn, to facilitate the categorization of artificial intelligence techniques. This results in a model that stands out for its ability to offer highly accurate forecasts by considering and quantifying the influence of precursors. The results of the model determined a Spearman correlation greater than 0.6, the root mean square error remained below 10 µg/m³ in all cases and the Fit Index exceeded the value of 0.5 in all cases, categorized as good , excellent and good respectively, which suggests that the model accurately and precisely replicates the behavior and trend of ozone. It is expected that the applied methodology will serve as a reference to continue with the prediction of other atmospheric pollutants of interest and in this way provide support for decision-making that allows minimizing environmental impacts focused on air quality.MaestríaMagíster en Ingeniería - Ingeniería AmbientalCalidad del aireviii, 101 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería AmbientalFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaOzono troposféricoMachine learningCalidad del airePredicciónPrecursoresTropospheric ozoneMachine LearningConvolutionalAir qualityContaminación atmosféricaaprendizaje automáticoModelo de simulaciónAir pollutionmachine learningSimulation modelsPredicción de ozono troposférico en Bogotá: un enfoque de Machine LearningTropospheric ozone prediction in Bogotá: a machine learning approachTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBogotáColombiaCundinamarcahttp://vocab.getty.edu/page/tgn/1000838Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Jeffrey Dean, D.; Devin, M.; Ghemawat, S.; et al. 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Journal of Electronic Imaging. doi:10.1117/1.JEI.28.2.023028Público generalORIGINALThesis_Msc_Diana_G.pdfThesis_Msc_Diana_G.pdfTesis de Maestría en Ingeniería - Ingeniería Ambientalapplication/pdf3806016https://repositorio.unal.edu.co/bitstream/unal/86187/2/Thesis_Msc_Diana_G.pdffaaf48db5e6facdd61c5239f03c47a23MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86187/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53unal/86187oai:repositorio.unal.edu.co:unal/861872024-05-30 13:44:18.282Repositorio Institucional Universidad Nacional de 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