The Prediction of PM2.5 Concentration Using Transfer Learning Based on ADGRU

Long-term exposure to air environments full of suspended particles, especially PM2.5, would damage people's health and life seriously. Nowadays, plenty of air quality monitoring networks have been established in several countries. Timely and accurate prediction of PM2.5 concentration is of great significance. However, current studies on PM2.5 concentration assume that the training set and testing set have the same data distribution, which leads to low accuracy and weak robustness of the model. In this paper, in order to predict accurately and solve the problem that the probability distribution of PM2.5 data may change over time, a feature transfer learning for time series model called adaptive GRU (ADGRU) is proposed. First, the training set data of Beijing PM2.5 concentration is divided into source domain data and target domain data. Then an adaptive bandwidth method is applied in ADGRU to improve its accuracy. Finally, extensive experimental simulations show that ADGRU can learn the time distribution changes of data efficiently, and predict the trend of PM2.5 concentration accurately, which may provide effective reference for people's travel and health.