Research on Air Quality Prediction Based onMultistep Time Series
Studyingthe level of air pollution and more accurate prediction of PM2.5 concentration and AQI index are of great significance for analyzing the influencing factors of pollution and formulating control strategies effectively.Based on pollutant concentrations and meteorological data inthe same region from 2015 to 2023,and according to the nonlinear and sequential characteristics of PM2.5 concentration and AQI,the multi-step prediction models of ARIMA and LSTM are constructed to predict the PM2.5 concentration and AQI grade.The results show that for the real data of PM2.5 concentration,the ARIMA model based on three-step prediction has the smallest RMSE value and is more suitable for the prediction of PM2.5 concentration,while in the real data set of AQI,LTSM model is more accurate than ARIMA model.
ARIMA modelLTSM modelair quality predictiontime series
万方数据
维普
