A reduced-form ensemble of short-term air quality forecasting with the Sparrow search algorithm and decomposition error correction.

Abstract

The level of air pollution is reflected by the air quality index (AQI). People can use the AQI to organize their activities in a way that reduces or prevents exposure to air pollution altogether. Based on the AQI, governments, organizations, and businesses can also make plans to reduce air pollution. The multi-model ensemble has recently become a popular method for forecasting time series; however, it encounters the research problems of multi-parameter optimization and interaction analysis. To this end, a reduced-form ensemble of short-term air quality forecasting with the Sparrow search algorithm and decomposition error correction model is proposed in this paper. First, the data are decomposed using the CEEMDAN decomposition algorithm. Second, the Sparrow search algorithm is used in the model training process to obtain the optimal hyperparameters of the deep learning model and construct the optimal deep learning model. Next, the constructed models are used to predict the decomposed data, and the Lagrange multiplier method is used to determine the weights of each deep learning model. At last, the prediction results of each deep learning model are combined according to the weights to obtain the combined prediction results. Experiments show that (1) GRU, Bi-GRU, LSTM, and Bi-LSTM are used to predict the undecomposed data and the data decomposed by CEEMADN. The outcomes demonstrate that the CEEMDAN decomposition technique can enhance the accuracy of the forecast, specifically an 11.248% reduction in average RMSE and a 0.865% increase in average R2. (2) A multi-model combination method based on the Lagrange multiplier method is designed, which can obtain the weights of each deep learning model, and the weights can combine multiple models. The results of the multi-model combination are better than those of the single model. (3) The Lagrange multiplier method was compared with the simple average combination model and the MAE inverse combination model. The experimental results show that the results obtained using the Lagrange multiplier method are better than the other two.