考虑径流过程矢量化的机器学习洪水预报模型  

Machine learning-based flood forecasting models considering runoff process vectorization

作　　者：刘成帅 解添宁 李文忠 胡彩虹[1] 徐源浩 牛超杰 余其鹰 LIU Chengshuai;XIE Tianning;LI Wenzhong;HU Caihong;XU Yuanhao;NIU Chaojie;YU Qiying(School of Water Conservancy and Transportation,Zhengzhou University,Zhengzhou 450001,China;School of Civil Engineering,Sun Yat-Sen University,Guangzhou 510275,China)

机构地区：[1]郑州大学水利与交通学院,河南郑州450001 [2]中山大学土木工程学院,广东广州510275

出　　处：《水科学进展》2024年第3期420-429,共10页Advances in Water Science

基　　金：国家重点研发计划资助项目(2023YFC3209303);国家自然科学基金资助项目(U2243219)。

摘　　要：准确的超前洪水预报有利于开展防洪减灾工作和优化水资源调度。本文提出一种针对场次洪水的径流过程矢量化方法(Runoff Process Vectorization, RPV),并耦合3种机器学习(Machine Learning, ML)模型构建了RPV-ML洪水预报系列模型。以黄河中上游孤山川、佳芦河和祖厉河3个典型流域为研究区,分别基于43、28、37场洪水的降雨径流数据,按照洪水场次7∶3的比例进行模型训练和验证。研究表明:(1)相同预见期条件下RPV-ML模型在孤山川、佳芦河和祖厉河流域洪水预报纳什效率系数更高、均方根误差和洪峰相对误差更低,RPV-ML模型比ML模型具有更好的预报性能,在预见期为4~6 h时优势更显著;(2) RPV-ML和ML模型预报精度会随着预见期增加逐渐下降,但RPV-ML预报精度呈现缓慢下降趋势,具有更好的鲁棒性;(3)基于RPV改进的时间卷积网络(Temporal Convolutional Network, TCN)可以更好地克服预报误差问题,RPV-TCN模型在3个流域预报性能最好。Accurate multi-step-ahead flood forecasting is beneficial for flood control and disaster reduction efforts,as well as optimizing water resource management.This study proposed a runoff process vectorization method(RPV)and combined it with three machine learning(ML)models to construct the RPV-ML flood forecasting series models.The research area comprises three typical river basins:Gushanchuan River,Jialu River,and Zuli River,in the middle and upper reaches of the Yellow River.Rainfall-runoff data from 43,28,and 37 flood events were respectively used for model training and validation,with a ratio of 7∶3 for flood events.The research shows that:①Under the same lead time conditions,RPV-ML has higher Nash efficiency coefficients(ENS),lower root mean square errors(ERMS),and lower relative errors(ER)of peak in flood forecasting for the Gushanchuan River,Jialu River,and Zuli River basins.RPV-ML outperforms ML models in terms of predictive performance,especially in lead times of 4-6 hours.②The accuracy of RPV-ML and ML models gradually decreases as the lead time increases,but RPV-ML exhibits a slower decline in accuracy and demonstrates better robustness.③The RPV-improved Temporal Convolutional Network(TCN)can better overcome forecasting errors,and the RPV-TCN model performs the best in terms of predictive performance among the three basins.The research findings can provide the scientific basis for flood control and disaster reduction efforts in river basins.

关 键 词：洪水预报 径流过程矢量化 机器学习 RPV-ML模型 黄河中上游 

分 类 号：P338[天文地球—水文科学]