WD-RBF Model and its Application of Hydrologic Time Series Prediction
Dengfeng Liu, Dong Wang, Yuankun Wang, Lachun Wang, Xinqing Zou · Journal of risk analysis and crisis response · 2013
Accurate prediction for hydrological time series is the precondition of water hazards prevention.A method of radial basis function network based on wavelet de-nosing (WD-RBF) was proposed according to the nonlinear problem and noise in hydrologic time series.Wavelet coefficients of each scale were calculated through wavelet transform; soft-threshold was used to eliminate error in series.Reconstructed series were predicted by RBF network.The simulation and prediction of WD-RBF model were compared with ARIMA and RBF network to show that wavelet de-nosing can identify and eliminate random errors in series effectively; RBF network can mine the nonlinear relationship in hydrologic time series.Examples show that WD-RBF model has superiority in accuracy compared with ARIMA and RBF network.