Air Pollutant Concentration Prediction Based on GRU Method

Abstract

In recent years, the Chinese government has paid more and more attention to the construction of ecological civilization, and the governance of air pollution is one of the necessary links. Aiming at the problem of the time series prediction of air pollutants, this paper develops a time series prediction model based on deep learning method. In this paper, Beijing’s hourly PM2.5 concentration information and weather information are used as input. Through GRU model, four models are trained according to the four seasons of spring, summer, autumn, and winter, and the effects of the four models on predicting the corresponding seasonal PM2.5 are evaluated by using corresponding test sets. After repeated experiments and constant adjustment of model parameters, the prediction error and prediction accuracy of the model are analyzed and compared, then the feasibility and advantages of this method are verified. The results delineate that the prediction accuracy of the model based on the GRU model is high, and the method is valid for the time series prediction of air pollutants.