WD-RBF Model and its Application of Hydrologic Time Series Prediction

DOI

10.2991/jrarc.2013.3.4.4How to use a DOI?

Keywords

Hydrologic time series, RBF network, Wavelet de-noising, Water hazards

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/jrarc.2013.3.4.4
ID  - Liu2013
ER  -