Hybrid Artificial Neural Network Coupled with Kalman Filters for Air Quality Forecasting in Guadalajara, Mexico
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Fecha
2018-03
Autores
González-Figueredo, Carlos
Egurrola-Hernández, E.A.
Ramírez-Briseño, R.L.
DeLosReyes-Corona, A.
DeAlba-Martínez, Hugo
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Descripción
This 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.
Palabras clave
Calidad del Aire, Filtros de Kalman, Redes Neuronales Artificiales
Citación
González-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.