A deep learning-based hybrid method for PM2.5 prediction in central and western China
Abstract To mitigate the adverse effects of air pollution, accurate PM2.5 prediction is particularly important. It is difficult for existing models to escape the limitations attached to a single model itself. This study proposes a hybrid PM2.5 prediction model utilizing deep learning techniques, whi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-95460-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850208343423975424 |
|---|---|
| author | Zuhan Liu Zihai Fang Yuanhao Hu |
| author_facet | Zuhan Liu Zihai Fang Yuanhao Hu |
| author_sort | Zuhan Liu |
| collection | DOAJ |
| description | Abstract To mitigate the adverse effects of air pollution, accurate PM2.5 prediction is particularly important. It is difficult for existing models to escape the limitations attached to a single model itself. This study proposes a hybrid PM2.5 prediction model utilizing deep learning techniques, which aims to complement each other’s strengths through model fusion. The model integrates the transformer and LSTM architectures and employs parameter optimization through the particle swarm optimization (PSO) algorithm. The proposed model achieves superior performance by utilizing the gating mechanism of the LSTM model, the positional encoding and self-attention mechanism of the Transformer model, and PSO’s robust optimization capabilities. Experimental results show that the new model outperforms both the traditional LSTM model and the PSO-LSTM model in the PM2.5 prediction task, and its evaluation metrics, R2, MAE, MBE, RMSE, and MAPE, are all improved. Furthermore, the model demonstrates stable performance across different cities and various periods. This study offers a robust approach to improving the accuracy and reliability of PM2.5 forecasting. |
| format | Article |
| id | doaj-art-e7ba92a585d74e268d7b02105af9d468 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e7ba92a585d74e268d7b02105af9d4682025-08-20T02:10:16ZengNature PortfolioScientific Reports2045-23222025-03-0115111710.1038/s41598-025-95460-6A deep learning-based hybrid method for PM2.5 prediction in central and western ChinaZuhan Liu0Zihai Fang1Yuanhao Hu2School of Information Engineering, Nanchang Institute of TechnologySchool of Information Engineering, Nanchang Institute of TechnologySchool of Information Engineering, Nanchang Institute of TechnologyAbstract To mitigate the adverse effects of air pollution, accurate PM2.5 prediction is particularly important. It is difficult for existing models to escape the limitations attached to a single model itself. This study proposes a hybrid PM2.5 prediction model utilizing deep learning techniques, which aims to complement each other’s strengths through model fusion. The model integrates the transformer and LSTM architectures and employs parameter optimization through the particle swarm optimization (PSO) algorithm. The proposed model achieves superior performance by utilizing the gating mechanism of the LSTM model, the positional encoding and self-attention mechanism of the Transformer model, and PSO’s robust optimization capabilities. Experimental results show that the new model outperforms both the traditional LSTM model and the PSO-LSTM model in the PM2.5 prediction task, and its evaluation metrics, R2, MAE, MBE, RMSE, and MAPE, are all improved. Furthermore, the model demonstrates stable performance across different cities and various periods. This study offers a robust approach to improving the accuracy and reliability of PM2.5 forecasting.https://doi.org/10.1038/s41598-025-95460-6Long short-term memory (LSTM)TransformerParticle swarm optimization (PSO)PM2.5 prediction |
| spellingShingle | Zuhan Liu Zihai Fang Yuanhao Hu A deep learning-based hybrid method for PM2.5 prediction in central and western China Scientific Reports Long short-term memory (LSTM) Transformer Particle swarm optimization (PSO) PM2.5 prediction |
| title | A deep learning-based hybrid method for PM2.5 prediction in central and western China |
| title_full | A deep learning-based hybrid method for PM2.5 prediction in central and western China |
| title_fullStr | A deep learning-based hybrid method for PM2.5 prediction in central and western China |
| title_full_unstemmed | A deep learning-based hybrid method for PM2.5 prediction in central and western China |
| title_short | A deep learning-based hybrid method for PM2.5 prediction in central and western China |
| title_sort | deep learning based hybrid method for pm2 5 prediction in central and western china |
| topic | Long short-term memory (LSTM) Transformer Particle swarm optimization (PSO) PM2.5 prediction |
| url | https://doi.org/10.1038/s41598-025-95460-6 |
| work_keys_str_mv | AT zuhanliu adeeplearningbasedhybridmethodforpm25predictionincentralandwesternchina AT zihaifang adeeplearningbasedhybridmethodforpm25predictionincentralandwesternchina AT yuanhaohu adeeplearningbasedhybridmethodforpm25predictionincentralandwesternchina AT zuhanliu deeplearningbasedhybridmethodforpm25predictionincentralandwesternchina AT zihaifang deeplearningbasedhybridmethodforpm25predictionincentralandwesternchina AT yuanhaohu deeplearningbasedhybridmethodforpm25predictionincentralandwesternchina |