水科学进展2025，Vol.36Issue(4)：634-645,12.DOI:10.14042/j.cnki.32.1309.2025.04.008
耦合深度学习与水文模型的喀斯特地区径流模拟方法
Runoff simulation in Karst regions by integrating deep learning with physically-based hydrological models
摘要
Abstract
The water cycle mechanism of Karst regions is complex,posing a significant challenge to the collection of geologic data.Thus,further improvement of the runoff simulation performance of hydrological models in these regions is a research challenge that requires attention.By coupling an improved EXP-HYDRO model,a two-layer NRIHM model,and a recurrent neural network algorithm,a hydrological model was developed that integrates the physical mechanisms and artificial intelligence(AI).Simulation and validation of the model were performed for two Karst regions:the Nanyang River basin in the middle reaches of the Yangtze River and the Yeji River basin in the middle reaches of the Wujiang River.The findings suggest that the coupled hydrological model can significantly improve the simulation accuracy of the daily runoff process in Karst areas.Compared with conventional hydrological models,the coupled model can improve the Nash-Sutcliffe efficiency by approximately 19.7%in the Nanyang River basin and 37.1%in the Yeji River basin.Moreover,owing to the learning of physical mechanisms,the coupled model exhibits better simulation accuracy than purely data-driven approaches,suggesting that the comprehensive prediction performance of AI models can be effectively improved by properly selecting and learning different physical mechanisms.关键词
耦合水文模型/物理递归神经网络/径流模拟/喀斯特地区Key words
hybrid model/physical process-wrapped recurrent neural network/runoff simulation/Karst region分类
天文与地球科学引用本文复制引用
许钦,金晨,张坤,陈星,张东杰..耦合深度学习与水文模型的喀斯特地区径流模拟方法[J].水科学进展,2025,36(4):634-645,12.基金项目
智慧长江与水电科学湖北省重点实验室开放基金(2422020009) （2422020009）
国家自然科学基金项目(U2443202)The study is financially supported by the Open Research Fund of Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science,China(No.2422020009)and the National Natural Science Foundation of China(No.U2443202). （U2443202）
