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基于深度学习的雷达降雨临近预报及洪水预报.
- Source :
-
Advances in Water Science / Shuikexue Jinzhan . Sep2023, Vol. 34 Issue 5, p673-684. 12p. - Publication Year :
- 2023
-
Abstract
- To explore the applicability of deep learning methods to radar rainfall nowcasting and flood forecasting, U-Net, Attention-Unet and TransAtt-Unet are used to carry out rainfall nowcasting. The nowcasted rainfall results are used as inputs to the HEC-HMS hydrological model for flood forecasting. The results show that with a 1-hour lead time, Attention-Unet has the best performance in nowcasting heavy rainfall with a short duration, and the relative errors in the simulated flood peak and runoff volume by the nowcasted rainfall of TransAtt-Unet are less than 20%. Each deep learning model has a good forecasting accuracy for rainfall and flood events with large magnitudes. The rainfall intensity, rainfall totals, flood peaks and runoff volumes are significantly underestimated with a 2-hour lead time, with U-Net achieving relatively good rainfall nowcasting. The 1-hour lead time radar rainfall nowcasting and flood forecasting based on deep learning can provide a scientific reference for watershed flood prevention and mitigation. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FLOOD forecasting
*RAINFALL
*DEEP learning
*RADAR
*WATERSHEDS
Subjects
Details
- Language :
- Chinese
- ISSN :
- 10016791
- Volume :
- 34
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Advances in Water Science / Shuikexue Jinzhan
- Publication Type :
- Academic Journal
- Accession number :
- 174281481
- Full Text :
- https://doi.org/10.14042/j.cnki.32.1309.2023.05.003