Long-term PM2.5 Concentrations Forecasting Based on Self-attention
In recent years,machine learning-based PM2.5 prediction has gradually become main-stream,with strong nonlinear modeling capabilities and advantages in improving prediction accu-racy.However,predicting PM2.5 concentration changes over a large time span still faces challen-ges.This study constructed multiple models with self-attention mechanisms,extending the daily prediction of PM2.5 concentration to a scale of 14 days and improving the prediction accuracy of PM2.5 on a daily basis.Comparative analysis of the Informer,Autoformer,FEDformer,and TCN models was conducted for long-term daily PM2.5 concentration prediction,enhancing the accuracy and reliability of PM2.5 prediction models.Three time scales were constructed in this study:3 days,7 days,and 14 days.The Auto former model performed the best at each time scale.Compared to the TCN model,the Autoformcr model showed significant performance improve-ments in predicting the future 3-day time scale,with RMSE and MAE reductions of 43.36％ and 42.70％,respectively.At the 7-day time scale,RMSE improved by 39.07％,and MAE im-proved by 8.98％.At the 14-day time scale,RMSE improved by 39.07％,and MAE improved by 8.98％.This study effectively improved the accuracy of PM2.5 prediction in long-term time scries forecasting.
NETL
NSTL
万方数据
