Title :
Multi-scale RBF Prediction Model of Runoff Based on EMD Method
Author :
Hui, Su ; Xinxia, Liu
Author_Institution :
Hydropower Res., Hebei Univ. of Eng., Handan, China
Abstract :
Runoff prediction is an important element in the study field of hydrology and water resources. Point to non-linear, chaotic character and with the noise characteristics Run-off signals, we propose a new model based on empirical mode decomposition (EMD) and the RBF neural network (RBF). First, runoff time series will be broken down into a series of different scales intrinsic mode function IMF by EMD, Second, the denoise and phase-space reconstruction will be done. The third, we predict each component by RBF. Finally, we reconstruct the final prediction value by each component. Simulation results show that the method have a high accuracy in denoising and prediction of the runoff sequence.
Keywords :
chaos; geophysical signal processing; hydrological techniques; radial basis function networks; signal denoising; water resources; EMD method; RBF neural network; empirical mode decomposition; hydrology; intrinsic mode function; multiscale RBF prediction model; noise characteristics; phase-space reconstruction; point to nonlinear chaotic character; runoff sequence prediction; runoff signal prediction model; water resources; Analytical models; Chaos; Cities and towns; Hydroelectric power generation; Hydrology; Noise reduction; Predictive models; Signal analysis; Space technology; Water resources; EMD; RBF; denoising; phase space reconstruction; prediction;
Conference_Titel :
Information and Computing (ICIC), 2010 Third International Conference on
Conference_Location :
Wuxi, Jiang Su
Print_ISBN :
978-1-4244-7081-5
Electronic_ISBN :
978-1-4244-7082-2
DOI :
10.1109/ICIC.2010.260