摘要
Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.
在分析非线性河道洪水预报方法中常用BP神经网络不足的基础上,采用具有快速收敛和更有效非线性逼近能力特性的小波神经网络.为适应洪水演进的时变特性,将所建立的用于河道洪水预报的小波神经网络与自回归实时校正模型耦合,校正值为小波神经网络预报值与自回归模型预报误差之和.自回归实时校正模型的参数通过自适应衰减因子递推最小二乘动态更新以提高校正效果.将该方法应用于西江高要断面洪水预报,计算结果验证了其有效性.
基金
The National Natural Science Foundation of China(No.50479017).
