Air pollutant prediction model based on transfer learning two-stage attention mechanism.
Saved in:
| Title: | Air pollutant prediction model based on transfer learning two-stage attention mechanism. |
|---|---|
| Authors: | Ma, Zhanfei, Wang, Bisheng, Luo, Wenli, Jiang, Jing, Liu, Dongxiang, Wei, Hui, Luo, HaoYe |
| Source: | Scientific Reports; 3/28/2024, Vol. 14 Issue 1, p1-18, 18p |
| Subject Terms: | AIR pollutants, STANDARD deviations, PREDICTION models, AIR pollution, DATA distribution |
| Abstract: | Atmospheric pollution significantly impacts the regional economy and human health, and its prediction has been increasingly emphasized. The performance of traditional prediction methods is limited due to the lack of historical data support in new atmospheric monitoring sites. Therefore, this paper proposes a two-stage attention mechanism model based on transfer learning (TL-AdaBiGRU). First, the first stage of the model utilizes a temporal distribution characterization algorithm to segment the air pollutant sequences into periods. It introduces a temporal attention mechanism to assign self-learning weights to the period segments in order to filter out essential period features. Then, in the second stage of the model, a multi-head external attention mechanism is introduced to mine the network's hidden layer key features. Finally, the adequate knowledge learned by the model at the source domain site is migrated to the new site to improve the prediction capability of the new site. The results show that (1) the model is modeled from the data distribution perspective, and the critical information within the sequence of periodic segments is mined in depth. (2) The model employs a unique two-stage attention mechanism to capture complex nonlinear relationships in air pollutant data. (3) Compared with the existing models, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the model decreased by 14%, 13%, and 4%, respectively, and the prediction accuracy was greatly improved. [ABSTRACT FROM AUTHOR] |
| Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Full Text 
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!