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Análisis de características tiempo-frecuencia para la predicción de series temporales de Material Particulado usando Regresión por Vectores de Soporte y Optimización por Enjambre de Partículas

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dc.contributor.authorSepulveda Suescun, Juan Pablospa
dc.contributor.authorAlzate Zuluaga, Norbey Yovanyspa
dc.contributor.authorMurillo Escobar, Juan Pablospa
dc.contributor.authorOrrego Metaute, Diana Alexandraspa
dc.contributor.authorCorrea Ochoa, Mauricio Andresspa
dc.date.accessioned2020-06-21 00:00:00
dc.date.accessioned2022-06-17T20:20:32Z
dc.date.available2020-06-21 00:00:00
dc.date.available2022-06-17T20:20:32Z
dc.date.issued2020-06-21
dc.description.abstractLa contaminación atmosférica por Material Particulado (PM) es un problema claramente reconocido a nivel mundial como uno de los factores de riesgo más importantes para la salud humana, en los últimos años han surgido diferentes modelos basados en inteligencia artificial para predecir la concentración de PM, con el fin de generar sistemas de alerta temprana que eviten la exposición de las personas. En este trabajo, se analizó un esquema de caracterización en el dominio tiempo-frecuencia usando la transformada Wavelet para la predicción de series temporales de PM10 y PM2.5 usando un algoritmo de Regresión por Vectores de Soporte optimizado por Enjambre de Partículas (SVR-PSO), además, se evaluó el efecto de la imputación de datos sobre las estimaciones. Los resultados obtenidos mostraron que, empleando características temporales, más las características tiempo-frecuencia propuestas, se obtiene el mejor desempeño de la SVR-PSO, además se encontró que el uso de la imputación de datos no afecta el desempeño de la SVR-PSO. El sistema propuesto en este trabajo permite disminuir el error de las estimaciones de concentración de PM10 y PM2.5 haciendo uso de características tiempo-frecuencia y es capaz de operar de forma robusta contra datos perdidos, aumentando su viabilidad de ser implementado en escenarios reales.spa
dc.description.abstractAtmospheric pollution by particulate matter is a problem recognized worldwide as a major risk factor for human health, over last years different models based on artificial intelligence has been proposed to forecast particulate matter concentration with the purpose of generate early warning systems that avoid people exposition. This paper analyzed a characterization scheme in time-frequency domain using the Wavelet to predict time series of PM10 and PM2.5 using the Support Vector Regression optimized with Particle Swarm Optimization (SVR-PSO). This paper also evaluated the effect of data imputation over estimations. Results showed that using time characteristics along with time-frequency characteristics SVR-PSO reach its best performance, also, it was found that use of data imputation does not affect SVR-PSO performance. The system proposed in this paper allow to estimate PM10 and PM2.5 concentrations with less error through time-frequency characteristics, in addition, it is capable to operate robustly against missing data, which improve its viability to be implemented in real scenarios.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.24050/reia.v17i34.1347
dc.identifier.eissn2463-0950
dc.identifier.issn1794-1237
dc.identifier.urihttps://repository.eia.edu.co/handle/11190/5095
dc.identifier.urlhttps://doi.org/10.24050/reia.v17i34.1347
dc.language.isospaspa
dc.publisherFondo Editorial EIA - Universidad EIAspa
dc.relation.bitstreamhttps://revistas.eia.edu.co/index.php/reveia/article/download/1347/1340
dc.relation.citationeditionNúm. 34 , Año 2020spa
dc.relation.citationendpage15
dc.relation.citationissue34spa
dc.relation.citationstartpage1
dc.relation.citationvolume17spa
dc.relation.ispartofjournalRevista EIAspa
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dc.rightsRevista EIA - 2020spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0spa
dc.sourcehttps://revistas.eia.edu.co/index.php/reveia/article/view/1347spa
dc.subjectSVRspa
dc.subjectPSOspa
dc.subjectTransformada Waveletspa
dc.subjectImputación de datosspa
dc.subjectPredicciónspa
dc.subjectRegresiónspa
dc.subjectSVReng
dc.subjectPSOeng
dc.subjectWavelet Transformeng
dc.subjectData imputationeng
dc.subjectPredictioneng
dc.subjectRegressioneng
dc.titleAnálisis de características tiempo-frecuencia para la predicción de series temporales de Material Particulado usando Regresión por Vectores de Soporte y Optimización por Enjambre de Partículasspa
dc.title.translatedTime-Frequency characteristics analysis for forecasting time series of particulate matter using Support Vector Regression and Particle Swarm Optimizationeng
dc.typeArtículo de revistaspa
dc.typeJournal articleeng
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dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
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dc.type.driverinfo:eu-repo/semantics/articlespa
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