Research on self-attention hybrid prediction method for PM2.5 concentration in subway stations
It is of great significance to establish a reliable air quality prediction model for economic de-velopment and pollution control.Since PM2.5 is the main pollutant in most parts of China,it has be-come a top priority to solve the problem of predicting PM2.5 concentration.In this paper,we propose an error correction model based on the self-attention mechanism to improve the prediction accuracy of PM2.5 concentration.This paper uses a self-attention mechanism to capture key information in the se-quence.The GRU is used to predict the sequence.The DBN is used to correct the error series to im-prove the accuracy and stability of the prediction,and the final prediction sequence is formed.In order to verify the performance of the model,this paper takes the outdoor PM2.5 data from Beijing,Tian-jin,Shanghai,and Guangzhou in China for metro stations as examples for data processing and predic-tion.The results show that the prediction model in this paper is superior to other reference models in terms of accuracy and stability,and provides a scientific basis for decision-makers to better control the problem of air pollution.
NETL
NSTL
万方数据
维普
