基于改进型前馈神经网络的流域产流预报模型的研究

在分析流域产流机制、影响因素和现行产流计算方法的基础上，首次取前期影响雨量、主产流历时、全过程面平均雨量和４个代表雨强计７个因子作为神经网络输入，直接以流域产流深作为神经网络输出，并针对传统ＢＰ算法的固有缺陷，采用混合ＧＮ－ＢＦＧＳ算法训练网络。实例验证了所建模型及算法的有效性和可行性。还对神经网络隐层单位数等进行了初步研究。

Researching on Watershed Runoff Forecasting Model Based on Revised Feedforward Neural Network

Under the analysis of the mechanism and influence elements of watershed runoff, the watershed runoff forecasting model based on the feedforward Neural Network (NN) is established in this paper. It selects seven factors as the inputs of NN, including antecedent precipitation,main runoff duration, four representative stations' rainfall intensity and area average rainfall,and runoff depth as the output.A new algorithm-hybrid GN-BFGS algorithm is introduced, which is better than the traditional BP method in calculating speed and convergence.The paper also discusses the hidden unit numbers of NN.