Forecasting urban air pollution using multi-site spatiotemporal data fusion method (Geo-BiLSTMA)

Abstract

The issue of air pollution is receiving more and more attention. And the key to effective monitoring and prediction of air pollution lies in combining spatial and temporal information from monitoring site data. In this study, we integrating the spatiotemporal characteristics of several sparse and irregular monitoring stations, which is essential for getting accurate air pollution prediction. Krigin interpolation method is used to generate geographical uniform grid surface data, then the historical data of the target site and its surrounding eight neighbors are extracted as the input of BiLSTM neural network which inducing attention mechanism, so we named it Geo-BiLSTMA. We experimented with the algorithm on air pollution datasets in Xi'an, and the results of the algorithm were compared with the five baseline algorithms. We also verified that the Geo-BiLSTMA model has a good generalization ability during the winter and summer prevention periods. And the algorithm has been deployed as an auxiliary model in the task of the national key R&D project.