基于支持向量机回归和小波变换的O<sub>3</sub>预报方法

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Abstract

In the paper, a hybrid model combining multivariable linear regression (MLR) and wavelet transformation (WT) to improve the support vector machine regression (SVR) forecast accuracy of hourly ozone (O₃) concentrations, by employing the observations of meteorological variables and O₃ and its precursors from June 1to August 15, 2016 at an industrial area in Nanjing. The original time series of O₃ concentration was decomposed by WT into a few sub-series with lower variability, the prediction strategy applied to each of them and then summed up all individual prediction results. The squared correlation coefficient (R²) of the forecast was 0.90. The mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean squared error (RMSE) were 3.86×10^-9, 28.26% and 5.57×10^-9, respectively. The results showed clearly that the performance of the M-WT-SVR was better than the M-SVR and SVR for hourly O₃ predictions. The low-level detail signals were mainly related to NO, NO₂ and aromatic hydrocarbons, while the coarse approximation signals at higher level were synthetically affected by meteorological conditions, precursors and O₃ pre-concentrations. Concerning the O₃ concentrations forecasting, M-WT-SVR gave significantly better predictions than MLR methods.

Key words： support vector machine regression wavelet transformation multivariable linear regression O₃ prediction hourly O₃ concentrations

Received: 25 February 2019

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X511

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	SU Xiao-qian
	AN Jun-lin
	ZHANG Yu-xin

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SU Xiao-qian,AN Jun-lin,ZHANG Yu-xin. Support vector machine regression forecasting of O₃ concentrations based on wavelet transformation[J]. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(9): 3719-3726.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2019/V39/I9/3719

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