基于BP神经网络的MIKE SHE模型参数率定

为了更精细地对水文全过程进行描述和解析,更准确地构建分布式水文模型,以丹麦Karup流域为例,对MIKE SHE模型的饱和导水率、饱和带水平水力传导系数、河床透水系数进行了参数率定,模拟流域的日径流过程。结果表明:基于BP神经网络反分析的参数率定方法比MIKE SHE模型参数自动率定计算得到的均方根误差RMSE小,模型效率系数Ens更接近1;采用BP神经网络反演率定参数后,3组测试样本的日径流模拟过程的RMSE分别为0.04,0.03,0.08 m³/s,Ens均为0.99,且模拟结果能较好地反映径流的实际变化趋势。因此,这种基于BP神经网络反分析的参数率定方法对构建分布式水文模型具有一定的价值。

Parameter Calibration of MIKE SHE Model Based on BP Neural Network

In order to describe and interpret hydrological processes in more detail, and at the same time to construct a more accurate distributed hydrological model, we took the Karup watershed in Denmark as an example and calibrated three parameters of MIKE SHE model, namely, saturated hydraulic conductivity, saturated horizontal hydraulic conductivity, and leakage coefficient of river bank, and simulated the daily runoff process in the watershed. Results demonstrate that the root mean square error (RMSE) obtained by the method of parameter calibration based on BP neural network is smaller than that by automatic parameter calibration in MIKE SHE model, with the model efficiency coefficient Ens closer to 1. Having been treated by parameter calibration by BP neural network, the values of RMSE of daily runoff of three test samples are 0.04 m³/s, 0.03 m³/s, and 0.08 m³/s, respectively, and the value of Ens is 0.99. As the simulated runoff displays a trend in agreement with the real runoff, the back analysis method of parameter calibration based on BP neural network is of certain value in runoff simulation.