Prediction of PM2.5 Concentration in Guangzhou Based on Bidirectional Long Short-term Memory Network
This paper proposes a prediction method of PM2.5 concentration based on bidirectional long short-term memory network,which can accurately predict PM2.5 concentration after one hour by using deep learning model.This method selects the input variables of the prediction model based on the feature importance of random forest calculation,assigns the weight of the data to reduce the prediction error,and builds a bidirectional long and short term memory network model to achieve the accurate prediction of PM2.5 concentration.Using the monitoring data of Panyu District and Nansha District of Guangzhou from 2021 to 2023 for verification and analysis,and compared with the traditional method using all input variables,the minimum root-mean-square error of the proposed scheme is reduced by 4.92%,the average absolute error is reduced by 7.57%,and the relative root-mean-square error is reduced by 4.92%.The proposed scheme can obtain higher prediction accuracy.
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