A hybrid prediction model of air quality for sparse station based on spatio-temporal feature extraction

Abstract

Accurate prediction of air quality is helpful for effective prevention and control of air pollution. However, there are relatively few studies on the air-quality prediction for sparse station. Through the extraction of spatio-temporal features, this research aims to achieve the hybrid prediction of air quality for sparse station. The proposed method is comprised of three parts. The first part is the extraction of multi-scale temporal features. Specifically, the multi-scale time lags of meteorological factors are extracted to cope with sudden changes, and the key features from multi-source data are extracted to avoid the interference of redundant features. The second part is to extract spatial features of related stations based on spatial hierarchy division. The third part is the hybrid model prediction based on spatio-temporal feature groups. The following results are obtained. (1) For sudden-change prediction, the processing method proposed in this paper is more effective. (2) Compared with the baselines and models popular in dense station, the proposed model has superior performance in the air-quality prediction of sparse stations. (3) Compared with the common spatial-related station selection method, the proposed method is more suitable for sparse stations. (4) For multi-step prediction, the proposed model has significant advantages in long-term prediction.