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Optimizing the performance of kalman filter based statistical time-varying air quality models
Hoi K.I.; Yuen K.V.; Mok K.M.
2010-03-01
Conference Name11th International Conference on Environmental Science and Technology (CEST2009)
Source PublicationGlobal Nest Journal
Volume12
Issue1
Pages27-39
Conference DateSEP 03-05, 2009
Conference PlaceChania, GREECE
PublisherGLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGY, 30 VOULGAROKTONOU STR, ATHENS, GR 114 72, GREECE
Abstract

In this study, the Bayesian approach is proposed to estimate the noise variances of Kalman filter based statistical models for predicting the daily averaged PM10 concentrations of a typical coastal city, Macau, with Latitude 22°10'N and Longitude 113°34'E. By using the measurements in 2001 and 2002, the Bayesian approach is capable to estimate the most probable values of the noise variances in the Kalman filter based prediction models. It turns out that the estimated process noise variance of the time-varying autoregressive model with exogenous inputs, TVAREX, is significantly (̃76%) less than that of the time-varying autoregressive model of order 1, TVAR(1), since the TVAREX model incorporates important mechanisms which govern the daily averaged PM concentrations in Macau. By further using data between 2003 and 2005, the choice of the noise variances is shown to affect the model performance, measured by the root-mean-squared error, of the TVAR(ρ) model and the TVAREX model. In addition, the optimal estimates of noise variances obtained by Bayesian approach for both models are located in the region where the model performance is insensitive to the choice of noise variances. Furthermore, the Bayesian approach will be demonstrated to provide more reasonable estimates of noise variances compared to the noise variances found by simply minimizing the root-mean-squared prediction error of the model. By comparing the optimized TVAREX model and the TVAR(ρ) models in predicting the daily averaged PM10 concentrations between 2003 and 2005, it is found that the TVAREX model outperforms the TVAR(ρ) models in terms of the general performance and the episode capturing capability. 

KeywordBayesian Inference Kalman Filter Macau Pm10 Time-varying Models
DOIhttps://doi.org/10.30955/gnj.000685
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000276798400005
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被引频次[WOS]:5   [WOS记录]     [WOS相关记录]
Document TypeConference paper
专题DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
AffiliationDepartment of Civil and Environmental Engineering, University of Macau, Av. Padre Tomás Pereira Taipa, Macau, China
First Author AffilicationUniversity of Macau
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Hoi K.I.,Yuen K.V.,Mok K.M.. Optimizing the performance of kalman filter based statistical time-varying air quality models[C]:GLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGY, 30 VOULGAROKTONOU STR, ATHENS, GR 114 72, GREECE,2010:27-39.
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