Predicción de ozono troposférico en Bogotá: un enfoque de Machine Learning

dc.contributor.advisorRojas Roa, Néstor Yesidspa
dc.contributor.advisorCasallas Garcia, Alejandrospa
dc.contributor.authorGarcía Millán, Diana Rocíospa
dc.contributor.researchgroupCalidad del Airespa
dc.coverage.cityBogotáspa
dc.coverage.countryColombiaspa
dc.coverage.regionCundinamarcaspa
dc.coverage.tgnhttp://vocab.getty.edu/page/tgn/1000838
dc.date.accessioned2024-05-30T18:40:40Z
dc.date.available2024-05-30T18:40:40Z
dc.date.issued2024-05
dc.descriptionilustraciones, diagramasspa
dc.description.abstractEl 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).spa
dc.description.abstractTropospheric 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.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Ingeniería Ambientalspa
dc.description.researchareaCalidad del airespa
dc.format.extentviii, 101 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/86187
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Ambientalspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaspa
dc.subject.proposalOzono troposféricospa
dc.subject.proposalMachine learningspa
dc.subject.proposalCalidad del airespa
dc.subject.proposalPredicciónspa
dc.subject.proposalPrecursoresspa
dc.subject.proposalTropospheric ozoneeng
dc.subject.proposalMachine Learningeng
dc.subject.proposalConvolutionaleng
dc.subject.proposalAir qualityeng
dc.subject.unescoContaminación atmosféricaspa
dc.subject.unescoAir pollutioneng
dc.subject.unescoaprendizaje automáticospa
dc.subject.unescomachine learningeng
dc.subject.unescoModelo de simulaciónspa
dc.subject.unescoSimulation modelseng
dc.titlePredicción de ozono troposférico en Bogotá: un enfoque de Machine Learningspa
dc.title.translatedTropospheric ozone prediction in Bogotá: a machine learning approacheng
dc.typeTrabajo de grado - Maestríaspa
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dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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