Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning

Xiang Xu

Journal of Forecasting, 2020, vol. 39, issue 2, 117-125

Abstract: PM2.5 mass concentration prediction is an important research issue because of the increasing impact of air pollution on the urban environment. In this paper, a PM2.5 forecasting framework incorporating meteorological factors based on multiple kernel learning (MKL) is proposed to forecast the near future PM2.5. In addition, we develop a novel two‐step algorithm for solving the primal MKL problem. Compared with most existing MKL 2‐step algorithms, the proposed algorithm does not require the optimal step size for updating kernel combination coefficients by linear search. To demonstrate the performance of the proposed forecasting framework, its performance is compared to single kernel‐based support vector regression (SVR). Data sets of an inland city Beijing acquired from UCI are used to train and validate both of two methods. Experiments show that our proposed method outperforms the SVR.

Date: 2020
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https://doi.org/10.1002/for.2599

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:39:y:2020:i:2:p:117-125

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