Air quality forecasting based on a sample convolution and interaction network model

Air quality forecasting is an effective means of managing and mitigating air pollution. To enhance prediction accuracy, a new air quality prediction model, namely the sample convolutional and interaction network(SCINet), is introduced in this paper. The model is composed of multiple SCI-Blocks arranged in a complete binary tree structure. Whereafter, the time series is rearranged through flipping odd-even splits, and a new sequence is generated,which is able to capture the complex dependencies and local trends of multivariate atmospheric pollutants better. Given the seasonality and randomness of monitoring data for atmospheric pollutant, the paper employs two SCINets for stacking, which not only expands the receptive field of convolutional operations, but also enables multi-resolution analysis. Furthermore, through the optimization of model depth and hyperparameters, the model may fit the temporal characteristics of air quality time series data better, which is helpful to extract the temporal relationship features of the target variable. In the end, the Beijing PM_2.5 dataset and the Beijing multi-site air quality dataset are utilized to evaluate the effectiveness of SCINet. The results show that SCINet has higher prediction accuracy, whose the root mean square error (<italic>δ</italic>_RMSE) is reduced by 31.59% in short-term prediction and 24.36% in long-term prediction compared with the best-performing DAQFF model.