水科学进展2023，Vol.34Issue(5)：673-684,12.DOI:10.14042/j.cnki.32.1309.2023.05.003
基于深度学习的雷达降雨临近预报及洪水预报
Radar rainfall nowcasting and flood forecasting based on deep learning
摘要
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.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Key words
radar rainfall nowcasting/quantitative rainfall estimation/deep learning/flood forecasting/Liulin experimental watershed分类
天文与地球科学引用本文复制引用
李建柱,李磊菁,冯平,唐若宜..基于深度学习的雷达降雨临近预报及洪水预报[J].水科学进展,2023,34(5):673-684,12.基金项目
国家自然科学基金资助项目(52279022)The study is financially supported by the National Natural Science Foundation of China(No.52279022). （52279022）
