河海大学学报（自然科学版）2025，Vol.53Issue(5)：1-9,9.DOI:10.3876/j.issn.1000-1980.2025.05.001
物理引导数据驱动方法研究综述及其在水文模型构建中的应用与展望
Research review of physics-guided data-driven methods and their application and prospects in hydrological model development
摘要
Abstract
To explore the fusion methods of physical mechanism models and data-driven models,the implementation pathway of existing physics-guided fusion driving methods was analyzed.The research status of physics-guided fusion driving methods based on theory-guided data science(TGDS)in different fields was classified and summarized,and new classification methods,such as the physics-guided feedback fusion driving method and the physics-guided coding fusion driving method,were proposed.According to the characteristics of the hydrological modeling field,the challenges of the physics-guided fusion driving method in hydrological model construction were elaborated in detail.And the future research directions have been prospectively discussed.It was believed that the physics-guided fusion driving method based on TGDS could enhance the physical consistency of prediction results,reduce the accumulation of prediction errors,and improve the interpretability of the model,providing a feasible path for improving flood forecasting-oriented hydrological models.It could not only improve the low prediction accuracy caused by the generalization process of the mechanism model but also effectively improve the interpretability problem caused by the excessive dependence of data-driven models on samples.However,this method faces the problems of limited computing power,inaccurate extraction of multi-source data features,and inflexible parameter adjustment in its application.Therefore,in future research,differentiable modelling(DM)can be combined,or large models combined with domain knowledge graphs can be used to further explore the modeling of flood time series prediction,so as to better meet the needs of flood forecasting in complex environments.关键词
物理引导数据驱动/融合驱动/洪水预报/人工智能Key words
physics-guided data driving/fusion driving/flood forecasting/artificial intelligence分类
天文与地球科学引用本文复制引用
冯钧,邵萍萍,张继茹,刘学毅..物理引导数据驱动方法研究综述及其在水文模型构建中的应用与展望[J].河海大学学报（自然科学版）,2025,53(5):1-9,9.基金项目
国家重点研发计划项目(2023YFC3209203) （2023YFC3209203）
国家自然科学基金项目(62306007) （62306007）
江苏省水利科技项目(2022002,2023044) （2022002,2023044）
水利部重大科技项目(SKS-2022132) （SKS-2022132）
