PM2.5 Concentration Prediction Based on the Combination of STL Decomposition and LSTM-RF Ensemble Model
Fine particulate matter(PM2.5)is closely related to atmospheric environment and hu-man health.In order to estimate PM2.5 concentration and pollution level in a timely and accurate manner,this study constructed STL-LSTM-RF models for PM2 5 concentration prediction.The experimental area selected was Shanghai,and hourly PM2 5 concentration data for 2021,as well as data on other air pollutants,and 3km*3km reanalysis data from the European Centre for Me-dium-Range Weather Forecasts,were used to conduct PM2.5 prediction experiments for 1-6 hours.The results show that the STL-LSTM-RF model has improved fitting effect compared to LSTM at all time scales,achieving good performance in predicting PM2 5 concentration for 1-6 hours and improving the ability to capture sudden changes in data.Due to the increase in lagged values of data,the predictive efficiency of LSTM has significantly decreased,while the decrease in simulation efficiency of the STL-LSTM-RF model is significantly smaller than that of LSTM.In summary,STL-LSTM-RF simulation can achieve more effective and accurate predic-tion of PM2.5 concentration,and can also provide technical support for people's daily travel,en-terprise production and life,and government decision-making.Therefore,it has great practical value.
NETL
NSTL
万方数据
