DocumentCode :
2959596
Title :
A comparison of bayesian and conditional density models in probabilistic ozone forecasting
Author :
Cai, Song ; Hsieh, William W. ; Cannon, Alex J.
Author_Institution :
Dept. of Earth & Ocean Sci., Univ. of British Columbia, Vancouver, BC
fYear :
2008
fDate :
1-8 June 2008
Firstpage :
2310
Lastpage :
2314
Abstract :
Probabilistic models were developed to provide predictive distributions of daily maximum surface level ozone concentrations. Five forecast models were compared at two stations (Chilliwack and Surrey) in the Lower Fraser Valley of British Columbia, Canada, with local meteorological variables used as predictors. The models were of two types, conditional density models and Bayesian models. The Bayesian models (especially the Gaussian Processes) gave better forecasts for extreme events, namely poor air quality events defined as having ozone concentration ges 82 ppb.
Keywords :
Bayes methods; air pollution; forecasting theory; meteorology; oxygen; probability; Bayesian density models; British Columbia; Canada; Gaussian processes; Lower Fraser Valley; conditional density models; forecast models; meteorological variables; poor air quality; predictive distributions; probabilistic models; probabilistic ozone forecasting; surface level ozone concentrations; Bayesian methods; Data mining; Gaussian distribution; Gaussian processes; Log-normal distribution; Meteorology; Predictive models; Springs; Testing; Weather forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2008.4634117
Filename :
4634117
Link To Document :
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