PM2.5 prediction using population-based centrality weight

The particulate matter (PM)2.5 forecasting has been being advanced with the development of deep learning methods. However, most of them do not consider the active population exposed to air pollution. We propose to apply a population-based centrality weight to the cost function of the forecasting model, reflecting both of residential and changes in active populations. This weight gives higher penalties for prediction errors in more and densely populated areas in terms of residential populations. Also, higher penalties are applied to areas with more active population. The proposed weight was applied to two types of deep learning models, the long-and-short term temporal neural network (LSTNet) and temporal-graph convolutional network (T-GCN) to forecast the PM2.5 in 25 districts of Seoul Metropolitan City in Korea for empirical experiments. The experimental results show that forecasting utilizing the population-based weight enhances not only accuracies in terms of the centrality-based evaluation metrics by around 2 – 7%, but also MAE, SMAPE, and R-squared score by around 1 – 7%. Moreover, further improvements in terms of such metrics were observed in forecasting for highly populated districts.