气象2025，Vol.51Issue(4)：446-459,14.DOI:10.7519/j.issn.1000-0526.2025.031301
深度学习技术在洪水预报中的应用进展及思考
Progress and Reflection on Application of Deep Learning Techniques in Flood Forecasting
摘要
Abstract
Flood forecasting is an effective non-engineering measure to reduce the economic losses brought by floods.Accurately forecasting flood is one of the key technical challenges in hydrological field.There are flood forecasting models based on physical mechanisms used,but the accuracy and efficiency of fore-casts need to be improved.At present,forecasting models constructed by using deep learning techniques have been developed rapidly.This article comprehensively reviews the principles and characteristics of deep learning models that have been applied in the field of flood forecasting and summarizes their application progresses and problems in the quantitative and probabilistic flood forecasting.In addition,this article ex-plores the relevance and application prospects of deep learning models combined with flood physics models,particularly in the parameterization of physical processes,interpretability studies,and error correction of flood forecasting models.The results suggest that the deep coupling of deep learning technology with physical models is the developing direction of deep learning models in the future.It will be an important development paradigm for the time series prediction of flood,and also an important research component to achieve intelligent water resource management in the future.Finally,to better apply the deep learning technology in the field of flood forecasting,some thoughts on the difficulties of deep learning in flood pre-diction are given and corresponding solutions are proposed for the current challenging problems.关键词
深度学习/洪水预报/定量预报/概率预报/耦合物理模型Key words
deep learning/flood forecasting/quantitative forecast/probabilistic forecast/coupling physical model分类
天文与地球科学引用本文复制引用
祁海霞,彭涛,智协飞,季焱,殷志远,沈铁元,王俊超,向怡衡,胡泊..深度学习技术在洪水预报中的应用进展及思考[J].气象,2025,51(4):446-459,14.基金项目
湖北省自然科学气象创新发展联合基金项目(2022CFD129、2023AFD094)、中国气象局创新发展专项(CXFZ2024PO43)、长江流域气象开放基金项目(CJLY2022Y06)、湖北省气象局面上基金项目(2022Y06)、贵州省科技厅自然科学面上项目(黔科合基础-zk[2025]面上319)、湖南省气象局重点项目(XQKJ22A005)、中国气象局流域强降水重点开放实验室基金项目(2023BHR-Y26)和中国气象局武汉暴雨研究所基本科研业务专项(WHIHRKYYW202404)共同资助 （2022CFD129、2023AFD094）
