DocumentCode :
2226660
Title :
Filtering and Multi-Scale RBF Prediction Model of Rainfall Based on EMD Method
Author :
Liu xinxia ; Zhang Anbing ; Shi Cuimei ; Wang haifeng
Author_Institution :
Hebei Univ. of Eng., Handan, China
fYear :
2009
fDate :
26-28 Dec. 2009
Firstpage :
3785
Lastpage :
3788
Abstract :
Rainfall prediction is a key question 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, rainfall 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 rainfall sequence.
Keywords :
hydrological techniques; radial basis function networks; rain; time series; water resources; weather forecasting; EMD method; RBF neural network; chaotic character; empirical mode decomposition; hydrology; imf; intrinsic mode function; multiscale RBF prediction model; noise characteristics Run-off signals; phase-space reconstruction; rainfall prediction; rainfall sequence; rainfall time series; water resources; Analytical models; Chaos; Data engineering; Information filtering; Information filters; Information science; Noise reduction; Predictive models; Signal analysis; Water resources;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4909-5
Type :
conf
DOI :
10.1109/ICISE.2009.592
Filename :
5455288
Link To Document :
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