Deep Spatio-Temporal Dense Network for Regional Pollution Prediction

Abstract

In most cities, the monitoring stations are sparse, and air pollution is affected by various internal and external factors. To promote the performance of regional pollution prediction, a deep spatio-temporal dense network model is proposed in this paper. First, the inverse distance weighted (IDW) space interpolation algorithm is used to construct historical emission data with insufficient station records. Based on the properties of spatial-temporal data, a deep spatial-temporal dense network model is designed to predict air pollution in each region. Finally, several experiments are conducted on the real dataset of Hangzhou. The results show that combing IDW spatial interpolation and deep spatial-temporal dense network model can effectively predict the regional air pollution and achieve superior performance compared with ARIMA, CNN, ST-ResNet, CNN-LSTM.