• DocumentCode
    522941
  • 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
  • Volume
    3
  • fYear
    2010
  • fDate
    4-6 June 2010
  • Firstpage
    296
  • Lastpage
    299
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • 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
  • Type

    conf

  • DOI
    10.1109/ICIC.2010.260
  • Filename
    5513982