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- W2941673670 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." @default.
- W2941673670 created "2019-05-03" @default.
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- W2941673670 date "2019-05-20" @default.
- W2941673670 modified "2023-09-24" @default.
- W2941673670 title "Forecasting air pollution PM <sub>2.5</sub> in Beijing using weather data and multiple kernel learning" @default.
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- W2941673670 doi "https://doi.org/10.1002/for.2599" @default.
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