Development And Application Of Neural Network Algorithm In Runoff Simulation

In the current hydrological research,the process-driven hydrological models are limited by reality,the input data required and obtained cannot match perfectly,which limits the application to a certain degree.However,the data-driven neural network models have good flexibility and excellent performance,which is warmly welcomed by the researchers.In the “Rainfall-Runoff” simulation,the runoff may be affected by the meteorological factors in the past.The method of lengthening simulation time step is adopted to solve this problem in the current simulation.The mainstream Artificial Neural Network(ANN)model has strong learning ability,but it cannot reproduce the fact that the influence of historical hydrological and meteorological events on future runoff will gradually decline,making the generalization ability of the model decline and obviously violates the laws of nature.In order to solve this problem,test its performance and find the best suitable application conditions,an improved neural network structure,Long-Short-Term-Memory(LSTM)neural network is applied on the upper Heihe River Basin in China,to make the modeling for the precipitation-runoff relationship on different time scales.For the daily modeling,the Nash-Sutcliffe Efficiency Coefficient(NSE)of LSTM is 0.8054,which is higher than the NSE of ANN for 0.7843.But in the monthly modeling,the NSE of ANN and LSTM are 0.9077 and 0.8421 respectively.Taking the generalization ability and robustness into consideration,the LSTM is more suitable for daily modeling while ANN is a better option for the monthly simulation.In addition,this study also compares the two neural network models with another process-driven hydrologic model(FLEX-Topo)and it is found that the NSE gaps among them are marginal,which proves that the network models are competitive.Finally,based on the conclusion that ANN is more suitable for the monthly scale simulation,this study uses the ANN model to estimate the different change trends of runoff under various future climate change scenarios.It is found that with the increase of temperature and precipitation,the uncertainty of water resources distribution in the upper Heihe River Basin will increase in the future,which also provides references for water resources management in the future climate change scenario.