A new decomposition-integrated air quality index prediction model.

Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [ABSTRACT FROM AUTHOR]