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- W2854730816 abstract "In order to adapt to the characteristics of high nonlinear and time-varying for air pollutants concentration and improve the real-time prediction accuracy of air pollutants concentration, a forecasting model of air pollutants concentration based on accurate online support vector regression (AOSVR) algorithm is established in this paper. According to the hourly SO <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</inf> concentration and meteorological parameters from May 2014 to April 2015 in Wanliu Monitoring Station of Beijing in China, the data of 2 months are selected as experimental samples. At the same time, Pearson correlation coefficient method is used to select the exogenous inputs which have strong correlation with the output variable. The results show that the AOSVR algorithm can adjust the prediction model dynamically, and the prediction accuracy is higher than that of the conventional fixed support vector regression (SVR) model." @default.
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- W2854730816 date "2018-06-01" @default.
- W2854730816 modified "2023-10-18" @default.
- W2854730816 title "Application of accurate online support vector regression in atmospheric SO<inf>2</inf> concentration prediction" @default.
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- W2854730816 doi "https://doi.org/10.1109/ccdc.2018.8408231" @default.
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