Runoff simulation in the upper Han River Basin using physics-informed machine learning-model
This study investigated the impact of coupling the conceptual hydrological model(GR4J)with the long short-term mem-ory model(LSTM)in a physics-informed machine learning(PIML)framework for runoff simulation.Three scenarios(Hl,H2 and H3)were designed to examine the effects of the physical model parameter feedback mechanism,the consideration of soil mois-ture as an intermediate variable,and the former both on the PIML models,respectively.The case study was conducted in the upper Han River Basin,with the Ankang hydrological station as the control station.The main findings were as follows:(1)Compared with the LSTM model,all three PIML models had improved performance on runoff simulation,with a 10.6％increase in average Nash-Sutcliffe efficiency(NSE)during the validation period.Additionally,both the PIML-H1 and PIML-H3 models exhibited bet-ter performance than the GR4J model,with a 4.2％increase in average NSE during the validation period.Notably,the PIML-H3 model outperformed other PIML models,indicating that coupling GR4J and LSTM models simultaneously considering intermediate variables and parameter feedback yielded the most significant improvement in the model performance of runoff simulation.(2)For low flows,all three PIML models outperformed the GR4J and LSTM models,and the PIML-H3 model achieved the best perform-ance.For high flows,the performance of all three PIML models was not high,implying that PIML models were suitable in simula-ting low flows events.(3)The runoff simulations from the three PIML models exhibited significantly seasonal variations during both the training and validation periods.The seasonal variations in the PIML-H2 and PIML-H3 models were more pronounced compared to that in the PIML-H1 model,indicating that the seasonal variations in simulated runoff results of the PIML model were influenced by intermediate variables.This study contributed to a better understanding of the performance differences among various PIML mod-el schemes in runoff simulation,providing technical support for runoff simulation and forecasting in the study area.
Physics-informed machine learningrunoff simulationintermediate variableGR4JLSTMHan River
万方数据
维普
