A new PM2.5 forecasting model based on data preprocessing, reinforcement learning and gated recurrent unit network

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Accurate PM2.5 forecasting is of great significance to atmosphere pollution monitoring and control. To accurately predict PM2.5 concentration, a novel hybrid model is proposed. Our novel model includes the following three modeling processes: In stage I, a novel secondary decomposition method is adopted to decompose the raw PM2.5 data into several subseries. In stage II, a feature selection method based on reinforcement learning selects optimal features of each subseries for the predictor. In stage III, the selected features are input into a gated recurrent unit network and output the final forecasting result of all subseries. The experimental results of the paper on different data sets have verified that: (1) The proposed feature selection method based on reinforcement learning can select the optimal features, and our method outperforms the traditional feature selection method in the forecasting accuracy; (2) The novel model has excellent prediction performance in all cases and can obtain the optimal forecasting accuracy compared with twenty benchmark models and three state-of-the-art models.