GCN-ST-MDIR: Graph Convolutional Network-Based Spatial-Temporal Missing Air Pollution Data Pattern Identification and Recovery
Yangwen Yu, Victor O. K. Li, Jacqueline C. K. Lam, Kelvin Chan
Abstract
Missing data pattern identification and recovery (MDIR) is vital for accurate air pollution monitoring. To recover the missing air pollution data, GCN-ST-MDIR, a Graph Convolutional Network (GCN)-based MDIR framework, is proposed to identify daily missing data patterns and automatically select the best recovery method. GCN-ST-MDIR presents four novelties: (1) A new graph construction is developed to improve GCN data representation for MDIR using S-T similarity matrix and domain-specific knowledge (e.g., weekend/weekday). (2) A TL component is used to pre-train LSCE and ILSCE models. (3) A GCN structure outputs a selection indicator to determine the dominant missing pattern for daily input. The pre-trained data recovery model's accuracy is incorporated into the GCN loss function to penalize the wrong indicator. (4) The output of the GCN structure is used as a score to combine LSCE and ILSCE. Results show that the domain-specific S-T regularity and irregularity can be used as the prior information for both GCN and ILSCE/LSCE to enhance feature extraction. Our model considerably improves the recovery performance as compared to the baselines. GCN-ST-MDIR has achieved an accuracy of 88.48% for general missing data recovery with consecutively and sporadically missing data. GCN-ST-MDIR can be extended to many other S-T MDIR challenges.