期刊
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
卷 58, 期 2, 页码 374-389出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2012.754102
关键词
instream flow; wavelet-neural network; Levenberg-Marquardt; Bayesian regularization
资金
- project Determination of Environmental Flow Requirement and its Safeguard Measures in the Wei River in China [2009DFA22980]
- project Effect of Physical and Chemical Actions on the Variation of Streambed Hydraulic Conductivity [51079123]
- National Natural Science Foundation of China
A wavelet-neural network (WNN) hybrid modelling approach for monthly river flow estimation and prediction is developed. This approach integrates discrete wavelet multi-resolution decomposition and a back-propagation (BP) feed-forward multilayer perceptron (FFML) artificial neural network (ANN). The Levenberg-Marquardt (LM) algorithm and the Bayesian regularization (BR) algorithm were employed to perform the network modelling. Monthly flow data from three gauges in the Weihe River in China were used for network training and testing for 48-month-ahead prediction. The comparison of results of the WNN hybrid model with those of the single ANN model show that the former is able to significantly increase the prediction accuracy.
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