Spatio-attention embedded recurrent neural network for air quality prediction

Predicting the air quality index (AQI) has been regarded as a critical problem for environmental control management. Many factors over time and space may relate to the diffusion of pollutants. In other words, there exist very intricate spatio-temporal interactions among the characteristic for revealing diffusion of pollutants. Recently, some relevant works studied the topic of AQI prediction considering spatial and temporal correlations simultaneously, but most of them either ignore geospatially topological structures to learn spatio-temporal dependency or utilize sub-modules separately to encode the spatial and temporal information. Unfortunately, ignoring geospatially topological structures or correlations among spatial properties and temporal dependencies leads that the AQI prediction model cannot deal with the prediction task well…