A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows

被引：67

作者：

Wei, Shouke ^{[1
,2
,3
]}

Yang, Hong ^{[1
]}

Song, Jinxi ^{[4
]}

Abbaspour, Karim ^{[1
]}

Xu, Zongxue ^{[5
]}

机构：

[1] Swiss Fed Inst Aquat Sci & Technol EAWAG, CH-8600 Dubendorf, Switzerland

[2] Emodlog Technol Inc, Vancouver, BC V5P 3R1, Canada

[3] Univ British Columbia, Dept Forest Resources Management, Vancouver, BC V6T 1Z4, Canada

[4] NW Univ Xian, Coll Urban & Environm Sci, Xian 710069, Peoples R China

[5] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China

来源：

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES | 2013年 / 58卷 / 02期

基金：

中国国家自然科学基金;

关键词：

instream flow; wavelet-neural network; Levenberg-Marquardt; Bayesian regularization; SUSPENDED SEDIMENT DATA; MONTHLY RAINFALL; STOCHASTIC GENERATION; CONJUNCTION MODEL; ANN; SIMULATION; TRANSFORMS;

D O I：

10.1080/02626667.2012.754102

中图分类号：

TV21 [水资源调查与水利规划];

学科分类号：

081501 ;

摘要：

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.

引用

页码：374 / 389

页数：16

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