水科学进展2023，Vol.34Issue(6)：839-849,11.DOI:10.14042/j.cnki.32.1309.2023.06.002
融合数据同化与机器学习的流域径流模拟方法
Catchment runoff simulation by coupling data assimilation and machine learning methods
摘要
Abstract
Accurate catchment runoff simulation under the changing environment has a great significance in the flood disaster prevention and regional water resources management.The machine learning(ML)approach has been widely and successfully applied in runoff modelling during recent years,which,however,has not yet fully considered the potential impact of changes in hydrological intermediate state variables.This study proposed a coupled ML-based model for runoff simulating by integrating the ensemble Kalman filter(EnKF),the principal component analysis(PCA)and the long short-term memory(LSTM),which denoted as EnKF-PCA-LSTM.The specific steps include:① The dynamic update of hydrological intermediate state variables via the EnKF method;② The integration of updated state variables into the input set for predictor selection by the PCA method;③Runoff simulation through the combination of chosen predictors with the LSTM model.Taking the Ganjiang River basin as a case study,we provided a comprehensive assessment on the runoff simulation performance of the EnKF-PCA-LSTM,and performed comparisons against that of the original LSTM model and the physical hydrological model HYMOD.Results show that the EnKF-PCA-LSTM outperforms both the LSTM and HYMOD models,as reflected by the higher Nash-Sutcliffe efficiency coefficients,the Kling-Gupta efficiency coefficient and the Nash-Sutcliffe efficiency for the log-transformed runoff(0.954,0.971 and 0.972,respectively).This finding suggests that considering the hydrological intermediate state could effectively improve the accuracy and stability of ML models in terms of runoff simulation,which undoubtedly provides valuable insight into the catchment runoff modeling.关键词
径流模拟方法/水文状态变量/集合卡尔曼滤波/主成分分析/长短时记忆神经网络Key words
runoff simulation approach/hydrological intermediate state variable/ensemble Kalman Filter/principal component analysis/long short-term memory分类
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邓超,陈春宇,尹鑫,王明明,张宇新..融合数据同化与机器学习的流域径流模拟方法[J].水科学进展,2023,34(6):839-849,11.基金项目
国家重点研发计划资助项目(2022YFC3202802) （2022YFC3202802）
中央高校基本科研业务费专项资金资助项目(B210201030)The study is financially supported by the National Key R&D Program of China(No.2022YFC3202802)and the Fundamental Research Funds for the Central Universities,China(No.B210201030). （B210201030）
