River channel flood forecasting method of coupling wavelet neural network with autoregressive model
Li Zhijia Zhou Yi Ma Zhenkun(College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China)
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 time-varying 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.