摘要
为探究深度学习的雷达降雨临近预报在流域洪水预报中的适用性,采用U-Net、嵌入注意力门的Attention-Unet和添加转换器的多级注意力TransAtt-Unet开展雷达降雨临近预报,将预报降雨作为HEC-HMS水文模型的输入,对柳林实验流域进行洪水预报。结果表明:1 h预见期时,Attention-Unet对短时强降雨预报结果较好,TransAtt-Unet预报降雨模拟的洪峰流量和径流量相对误差小于20%,各深度学习模型对量级较大的降雨和洪水预报精度较高;2 h预见期的预报降雨强度、降雨总量、洪峰流量和径流量存在显著低估,U-Net能取得相对较好的降雨预报结果。基于深度学习的1 h预见期雷达降雨临近预报及洪水预报可为流域防洪减灾提供科学依据。
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.
作者
李建柱
李磊菁
冯平
唐若宜
LI Jianzhu;LI Leijing;FENG Ping;TANG Ruoyi(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350,China)
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2023年第5期673-684,共12页
Advances in Water Science
基金
国家自然科学基金资助项目(52279022)。
关键词
雷达降雨临近预报
降雨定量估计
深度学习
洪水预报
柳林实验流域
radar rainfall nowcasting
quantitative rainfall estimation
deep learning
flood forecasting
Liulin experimental watershed
