A Deep Learning Model with Axial Attention for Radar Echo Extrapolation
Radar echo extrapolation is an important approach in precipitation nowcasting which utilizes historical radar echo images to predict future echo images. In this paper, we introduce the self-attention mechanism into Trajectory Gated Recurrent Unit (TrajGRU) model. Under the sequence-to-sequence frame...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2311003 |
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| author | Yu-Mei Xie Ying-Liang Zhao Shu-Yan Huang |
| author_facet | Yu-Mei Xie Ying-Liang Zhao Shu-Yan Huang |
| author_sort | Yu-Mei Xie |
| collection | DOAJ |
| description | Radar echo extrapolation is an important approach in precipitation nowcasting which utilizes historical radar echo images to predict future echo images. In this paper, we introduce the self-attention mechanism into Trajectory Gated Recurrent Unit (TrajGRU) model. Under the sequence-to-sequence framework, we have developed a novel convolutional recurrent neural network called Self-attention Trajectory Gated Recurrent Unit (SA-TrajGRU), which incorporates the self-attention mechanism. The SA-TrajGRU model which combines spatiotemporal variant structure in TrajGRU and self-attention module is simple and effective. We evaluate our approach on the Moving MNIST-2 dataset and the CIKM AnalytiCup 2017 radar echo dataset. The experimental results show that the performance of the proposed SA-TrajGRU model is comparable to other convolutional recurrent neural network models. HSS and CSI scores of the SA-TrajGRU model are higher than scores of other models under the radar echo threshold of 25 dBZ, indicating that the SA-TrajGRU model has the most accurate prediction results under this threshold. |
| format | Article |
| id | doaj-art-ef6bc77075a9415c8e8aa0d948765dda |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-ef6bc77075a9415c8e8aa0d948765dda2025-08-20T01:56:56ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2311003A Deep Learning Model with Axial Attention for Radar Echo ExtrapolationYu-Mei Xie0Ying-Liang Zhao1Shu-Yan Huang2College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou, ChinaSchool of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, ChinaCollege of Electronics and Information Science, Fujian Jiangxia University, Fuzhou, ChinaRadar echo extrapolation is an important approach in precipitation nowcasting which utilizes historical radar echo images to predict future echo images. In this paper, we introduce the self-attention mechanism into Trajectory Gated Recurrent Unit (TrajGRU) model. Under the sequence-to-sequence framework, we have developed a novel convolutional recurrent neural network called Self-attention Trajectory Gated Recurrent Unit (SA-TrajGRU), which incorporates the self-attention mechanism. The SA-TrajGRU model which combines spatiotemporal variant structure in TrajGRU and self-attention module is simple and effective. We evaluate our approach on the Moving MNIST-2 dataset and the CIKM AnalytiCup 2017 radar echo dataset. The experimental results show that the performance of the proposed SA-TrajGRU model is comparable to other convolutional recurrent neural network models. HSS and CSI scores of the SA-TrajGRU model are higher than scores of other models under the radar echo threshold of 25 dBZ, indicating that the SA-TrajGRU model has the most accurate prediction results under this threshold.https://www.tandfonline.com/doi/10.1080/08839514.2024.2311003 |
| spellingShingle | Yu-Mei Xie Ying-Liang Zhao Shu-Yan Huang A Deep Learning Model with Axial Attention for Radar Echo Extrapolation Applied Artificial Intelligence |
| title | A Deep Learning Model with Axial Attention for Radar Echo Extrapolation |
| title_full | A Deep Learning Model with Axial Attention for Radar Echo Extrapolation |
| title_fullStr | A Deep Learning Model with Axial Attention for Radar Echo Extrapolation |
| title_full_unstemmed | A Deep Learning Model with Axial Attention for Radar Echo Extrapolation |
| title_short | A Deep Learning Model with Axial Attention for Radar Echo Extrapolation |
| title_sort | deep learning model with axial attention for radar echo extrapolation |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2311003 |
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