CoupledGT: Coupled Geospatial-temporal Data Modeling for Air Quality Prediction

Abstract

Air pollution seriously affects public health, while effective air quality prediction remains a challenging problem since the complex spatial-temporal couplings exist in multi-area monitoring data of the city. Current approaches rarely consider relative geographical locations when capturing spatial-temporal relations, instead the latent inter-dependencies (i.e., implicit spatial relations) of data as a replacement. However, such relations cannot necessarily reflect the diffusion of air pollutants in the real world, and genuine location-related information could be lost during the implicit relation learning process. In this article, we introduce a new concept, geospatial-temporal data, and propose a novel deep neural network architecture, CoupledGT, to learn the geospatial-temporal couplings within data for air quality prediction. Specifically, the asymmetric diffusion relation of air quality data between two areas is first explicitly represented by the newly developed planar Gaussian diffusion (PGD) equation. And then, a geospatial couplings diffuser (GCD) is designed to parameterize the PGD equation and learn multi-areas diffusion mutually affected geospatial couplings. Besides, the RNN is employed to capture temporal couplings of each area, and incorporated with GCD to learn both shared and unique characteristics of the geospatial-temporal data simultaneously, which empowers the generalization and efficiency of the model. Extensive experiments on two real-world datasets demonstrate our method is robust and outperforms existing baseline methods in air quality prediction tasks.