Research on Air Quality Prediction Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism

Abstract

A method of air quality prediction based on deep learning is proposed in this paper, that is an air quality prediction model combining bidirectional gated recurrent unit and attention mechanism. Taking cities with air quality monitoring stations as reference, the change trend of air quality index in the future is predicted by analyzing and processing the historical values of PM2.5, PM10, SO2, NO2, O3, CO in the past five years. The experimental results show that the model has good results in the mean absolute percentage error, mean absolute error and root mean square error of air quality prediction, and the model can effectively improve the data accuracy and stability of urban air quality prediction.