Volume 3, Issue 4, December 2013, Pages 185 - 191
WD-RBF Model and its Application of Hydrologic Time Series Prediction
Dengfeng Liu, Dong Wang, Yuankun Wang, Lachun Wang, Xinqing Zou
Received 15 September 2013, Accepted 12 December 2013, Available Online 27 December 2013.
- https://doi.org/10.2991/jrarc.2013.3.4.4How to use a DOI?
- Hydrologic time series, RBF network, Wavelet de-noising, Water hazards
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Dengfeng Liu AU - Dong Wang AU - Yuankun Wang AU - Lachun Wang AU - Xinqing Zou PY - 2013 DA - 2013/12/27 TI - WD-RBF Model and its Application of Hydrologic Time Series Prediction JO - Journal of Risk Analysis and Crisis Response SP - 185 EP - 191 VL - 3 IS - 4 SN - 2210-8505 UR - https://doi.org/10.2991/jrarc.2013.3.4.4 DO - https://doi.org/10.2991/jrarc.2013.3.4.4 ID - Liu2013 ER -