Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China
Efficient and accurate air quality forecasting contributes significantly to environmental governance, health promotion, and the development of smart cities. However, few existing models can achieve multi-scale feature fusion and long sequence time series modeling in the prediction process. This stud...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10680084/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850263533442301952 |
|---|---|
| author | Wenyi Cao Rufei Zhang Wenxin Cao |
| author_facet | Wenyi Cao Rufei Zhang Wenxin Cao |
| author_sort | Wenyi Cao |
| collection | DOAJ |
| description | Efficient and accurate air quality forecasting contributes significantly to environmental governance, health promotion, and the development of smart cities. However, few existing models can achieve multi-scale feature fusion and long sequence time series modeling in the prediction process. This study proposes a PatchTST-Enhanced model for multi-site air quality forecasting based on spatiotemporal distribution. It uses daily air quality data from 11 prefecture-level cities in Hebei Province from December 2, 2013, to October 12, 2023, to train the model. The new model demonstrates robust performance in Hebei’s air quality forecasting (PreLen =96: MSE =0.5408, MAE =0.5408, RSE =0.5408; PreLen =192: MSE =0.2795, MAE =0.2795, RSE =0.2795; PreLen =336: MSE =0.6779, MAE =0.6779, RSE =0.6779; PreLen =720: MSE =0.6779, MAE =0.6779, RSE =0.6779), and the prediction accuracy has been significantly improved compared to both the pre-optimization model and other existing models. The PatchTST-Enhanced outperforms the PatchTST and improves it through four optimization modules: CGAttention, SiLU activation, AdamW optimizer, and SmoothL1 Loss function. By incorporating spatiotemporal features, the PatchTST-Enhanced can address the challenge of combining spatial and large temporal scales in air quality forecasting. The results provide critical information to protect health and improve the environment for the public. |
| format | Article |
| id | doaj-art-4d3196952bd645c5909b6725faa3ed07 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4d3196952bd645c5909b6725faa3ed072025-08-20T01:54:57ZengIEEEIEEE Access2169-35362024-01-011213203813205510.1109/ACCESS.2024.346018710680084Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in ChinaWenyi Cao0https://orcid.org/0009-0007-3257-4569Rufei Zhang1https://orcid.org/0000-0001-5590-3109Wenxin Cao2https://orcid.org/0009-0005-2562-6397School of Economics, Hebei GEO University, Shijiazhuang, ChinaSchool of Economics, Hebei GEO University, Shijiazhuang, ChinaSchool of Foreign Languages, Southwest Jiaotong University, Chengdu, ChinaEfficient and accurate air quality forecasting contributes significantly to environmental governance, health promotion, and the development of smart cities. However, few existing models can achieve multi-scale feature fusion and long sequence time series modeling in the prediction process. This study proposes a PatchTST-Enhanced model for multi-site air quality forecasting based on spatiotemporal distribution. It uses daily air quality data from 11 prefecture-level cities in Hebei Province from December 2, 2013, to October 12, 2023, to train the model. The new model demonstrates robust performance in Hebei’s air quality forecasting (PreLen =96: MSE =0.5408, MAE =0.5408, RSE =0.5408; PreLen =192: MSE =0.2795, MAE =0.2795, RSE =0.2795; PreLen =336: MSE =0.6779, MAE =0.6779, RSE =0.6779; PreLen =720: MSE =0.6779, MAE =0.6779, RSE =0.6779), and the prediction accuracy has been significantly improved compared to both the pre-optimization model and other existing models. The PatchTST-Enhanced outperforms the PatchTST and improves it through four optimization modules: CGAttention, SiLU activation, AdamW optimizer, and SmoothL1 Loss function. By incorporating spatiotemporal features, the PatchTST-Enhanced can address the challenge of combining spatial and large temporal scales in air quality forecasting. The results provide critical information to protect health and improve the environment for the public.https://ieeexplore.ieee.org/document/10680084/Air quality forecastatmospheric pollutantsspatiotemporal distributionPatchTST-enhanceddeep learning |
| spellingShingle | Wenyi Cao Rufei Zhang Wenxin Cao Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China IEEE Access Air quality forecast atmospheric pollutants spatiotemporal distribution PatchTST-enhanced deep learning |
| title | Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China |
| title_full | Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China |
| title_fullStr | Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China |
| title_full_unstemmed | Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China |
| title_short | Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China |
| title_sort | multi site air quality index forecasting based on spatiotemporal distribution and patchtst enhanced evidence from hebei province in china |
| topic | Air quality forecast atmospheric pollutants spatiotemporal distribution PatchTST-enhanced deep learning |
| url | https://ieeexplore.ieee.org/document/10680084/ |
| work_keys_str_mv | AT wenyicao multisiteairqualityindexforecastingbasedonspatiotemporaldistributionandpatchtstenhancedevidencefromhebeiprovinceinchina AT rufeizhang multisiteairqualityindexforecastingbasedonspatiotemporaldistributionandpatchtstenhancedevidencefromhebeiprovinceinchina AT wenxincao multisiteairqualityindexforecastingbasedonspatiotemporaldistributionandpatchtstenhancedevidencefromhebeiprovinceinchina |