基于Elman动态神经网络的降雨—径流模拟研究

采用Elman动态神经网络对沂沭河流域上游临沂子流域日径流量进行模拟。为了更好地检验该网络估测径流的精度,同时采用陆面水文过程模型TOPX进行对比分析。确定性线系数、相关系数、平均相对误差和平均相对均方根误差四个统计指数及流域径流过程。结果表明,Elman动态神经网络能够对日径流量进行较好的模拟,较好地捕捉洪峰流量和出现时间,为降雨径流模拟提供了一种有效可靠的方法。

Application of rainfall-runoff simulation based on Elman recurrent dynamic neural network model

An Elman recurrent neural network model(ENN) is constructed and applied to the daily runoff forecast in the Linyi sub-catchment of upper Yishu river basin in this paper.In order to further evaluate the performance of ENN,land surface hydrological model TOPX is applied as a comparison at the same time in the study region.Based on analysis indexes such as Nash-Sutcliffe coefficient,correlation coefficient,mean relative error and root mean relative square error,the results of daily runoff and flooding processes indicated that ENN presents high accuracy in hydrological simulations on the rainfall-runoff dynamic process,the peak flow and peak occurrence time.It is feasible to take it as a promising and efficient method to simulate the daily runoff.