PM2.5 hourly concentration prediction based on graph capsule networks

In this paper, we use a graph capsule network to capture the spatial dependence of air quality data and meteorological data among cities, then use an LSTM network to model the temporal dependence of air pollution levels in specific cities and finally implement PM2.5 concentration prediction. We propose a graph-capsule-LSTM model based on a graph-capsule network and an LSTM network. The model uses a graph capsule network to model the neighboring feature information of the target city and then combines the local data of the target city to form the final feature vector. The feature mapping on the time axis is then used to obtain the temporal feature sequences of the target nodes, which are fed into the LSTM network for learning and prediction. Experiments show that the method achieves better results than the latest baseline model in the PM2.5 prediction task. While demonstrating that the capsule network outperforms the convolutional network, it also shows that this capsule network is very competent for the task of PM2.5 prediction.