Chapter
Jun 29, 2016
Comparative Analysis on the Prediction Model of Near-Road CO and PM2.5 Concentration
Authors: Han Lyu [email protected], Ya Gao [email protected], and Zhizhou Wu [email protected]Author Affiliations
Publication: CICTP 2016
Abstract
The time series of particulate matter at urban intersection consists of complex linear and non-linear patterns and are difficult to predict. Wavelet neural network (WNN) model has been applied to air quality prediction in urban areas, but it has limited accuracy owing to the co-linearity between the input variables. To overcome it, a novel hybrid model combining WNN model and factor analysis (FA) is proposed to improve the prediction accuracy. The FC was applied before the WNN model was implemented to generate principal components as input variables, rather than using the original data, to reduce the complexity and eliminate data co-linearity.
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Published In
CICTP 2016
Pages: 1951 - 1966
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© 2016 American Society of Civil Engineers.
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Published online: Jun 29, 2016
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College of Transportation Engineering, Tongji Univ., 4800 Cao’an Rd., Jiading, Shanghai, China. E-mail: [email protected]
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong Univ. E-mail: [email protected]
College of Transportation Engineering, Tongji Univ., 4800 Cao’an Rd., Jiading, Shanghai, China. E-mail: [email protected]
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