Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model
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| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1186/s42834-023-00175-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8045 |
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oai:localhost:PNK-80452023-04-18T08:14:07Z Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model Kun, Cai Xusheng, Zhang Ming, Zhang AE-Informer model implements the AE CC BY Air pollution is an important issue affecting sustainable development in China, and accurate air quality prediction has become an important means of air pollution control. At present, traditional methods, such as deterministic and statistical approaches, have large prediction errors and cannot provide effective information to prevent the negative effects of air pollution. Therefore, few existing methods could obtain accurate air pollutant time series predictions. To this end, a deep learning-based air pollutant prediction method, namely, the autocorrelation error-Informer (AE-Informer) model, is proposed in this study. The model implements the AE based on the Informer model. The AE-Informer model is used to predict the hourly concentrations of multiple air pollutants, including PM10, PM2.5, NO2, and O3. 2023-04-18T08:14:07Z 2023-04-18T08:14:07Z 2023 Book https://link.springer.com/article/10.1186/s42834-023-00175-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8045 en application/pdf Springer |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
AE-Informer model implements the AE |
| spellingShingle |
AE-Informer model implements the AE Kun, Cai Xusheng, Zhang Ming, Zhang Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model |
| description |
CC BY |
| format |
Book |
| author |
Kun, Cai Xusheng, Zhang Ming, Zhang |
| author_facet |
Kun, Cai Xusheng, Zhang Ming, Zhang |
| author_sort |
Kun, Cai |
| title |
Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model |
| title_short |
Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model |
| title_full |
Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model |
| title_fullStr |
Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model |
| title_full_unstemmed |
Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model |
| title_sort |
improving air pollutant prediction in henan province, china, by enhancing the concentration prediction accuracy using autocorrelation errors and an informer deep learning model |
| publisher |
Springer |
| publishDate |
2023 |
| url |
https://link.springer.com/article/10.1186/s42834-023-00175-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8045 |
| _version_ |
1763543258269483008 |
| score |
8.891145 |