Lanzhou University of Technology Institutional Repository (LUT_IR)
Dynamic Residual Attention UNet for Precipitation Nowcasting Based on WGAN | |
Li, Ce; Huang, Fan; Zhang, Jianwei![]() ![]() | |
2023 | |
会议名称 | 2023 China Automation Congress, CAC 2023 |
会议录名称 | Proceedings - 2023 China Automation Congress, CAC 2023
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页码 | 6265-6270 |
会议日期 | November 17, 2023 - November 19, 2023 |
会议地点 | Chongqing, China |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
摘要 | In recent years, using radar echo maps for precipitation nowcasting has been a research hotspot. How to use deep learning methods to forecast precipitation is a challenge. Radar echo map contains rich temporal and spatial information, capturing the location distribution and intensity characteristics of radar echo is a key problem in precipitation prediction. To tackle these challenges, the paper presents a novel approach called the Dynamic Residual Attention UNet model(DRA-UNet). This model incorporates Decoupled Dynamic Filter(DDF) and Dynamic Residual Attention Modules(DRAM) while leveraging the Wasserstein GAN training strategy to perform generative adversarial training. A decoupled Dynamic Filter can adaptively adjust the convolution kernel in the feature extraction stage, effectively reducing blank areas in the feature maps. By exploring the correlation between residual paths and input image statistics, and appropriately weighting each residual path, the model's focus on precipitation positions is enhanced. Moreover, the utilization of the Wasserstein GAN(WGAN) strategy during model training enhances the image generation quality when facing the discriminator in adversarial training. This advancement ensures that the model's outputs closely approximate real results, leading to further improvements in overall model performance. We comprehensively evaluate the performance of our model on the KNMI dataset, and a large number of experimental results show that our method achieves remarkable results on the precipitation prediction task. © 2023 IEEE. |
关键词 | Deep learning Forecasting Large datasets Learning systems Decoupled dynamic filter Dynamic filter Dynamic residual attention Hotspots Learning methods Nowcasting Precipitation nowcasting Precipitation predictions Radar echoes Wasserstein GAN |
DOI | 10.1109/CAC59555.2023.10450446 |
收录类别 | EI |
语种 | 英语 |
EI入藏号 | 20241515852553 |
EI主题词 | Image enhancement |
EI分类号 | 461.4 Ergonomics and Human Factors Engineering ; 723.2 Data Processing and Image Processing |
原始文献类型 | Conference article (CA) |
引用统计 | 无
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文献类型 | 会议论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/170546 |
专题 | 图书馆 外国语学院 |
通讯作者 | Li, Ce |
作者单位 | School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, China |
第一作者单位 | 兰州理工大学 |
通讯作者单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Li, Ce,Huang, Fan,Zhang, Jianwei,et al. Dynamic Residual Attention UNet for Precipitation Nowcasting Based on WGAN[C]:Institute of Electrical and Electronics Engineers Inc.,2023:6265-6270. |
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