Hybrid Artificial Neural Network Coupled with Kalman Filters for Air Quality Forecasting in Guadalajara, Mexico

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dc.contributor.authorGonzález-Figueredo, Carlos
dc.contributor.authorEgurrola-Hernández, E.A.
dc.contributor.authorRamírez-Briseño, R.L.
dc.contributor.authorDeLosReyes-Corona, A.
dc.contributor.authorDeAlba-Martínez, Hugo
dc.date.accessioned2019-08-28T18:16:35Z
dc.date.available2019-08-28T18:16:35Z
dc.date.issued2018-03
dc.descriptionThis study aims to develop a novel hybrid scheme of Artificial Neural Networks (ARN) coupled to a non-linear Kalman filter for air quality forecasting in Guadalajara Metropolitan Area, in Mexico. ARN’s are widely used for air quality forecasting, however these schemes need large amounts of data regarding the pollutants concentration levels and meteorological data in order to manage reliable forecasting. To address this issue, we present a scheme consisting of Neural Network models assisted by nonlinear Kalman filter that manage to considerably improve the forecasting performance, adding robustness in case of lack of data, and reducing the need of retraining over time.es
dc.identifier.citationGonzález-Figueredo, C., Egurrola-Hernández, E. A., Ramírez-Briseño, R. L., DeLosReyes-Corona, A., & DeAlba-Martínez, H. (2018). Hybrid Artificial Neural Network Coupled with Kalman Filters for Air Quality Forecasting in Guadalajara, Mexico. In Proceedings of Abstracts11th International Conference on Air Quality Science and Application, University of Hertfordshire.es
dc.identifier.isbn978-1-5272-2150-5
dc.identifier.urihttp://hdl.handle.net/11117/5989
dc.language.isoenges
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectCalidad del Airees
dc.subjectFiltros de Kalmanes
dc.subjectRedes Neuronales Artificialeses
dc.titleHybrid Artificial Neural Network Coupled with Kalman Filters for Air Quality Forecasting in Guadalajara, Mexicoes
dc.typeinfo:eu-repo/semantics/conferencePaperes
rei.peerreviewedSies

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