Flood forecasting using radial basis function neural networks

被引：50

作者：

Chang, FJ ^{[1
]}

Liang, JM

Chen, YC

机构：

[1] Natl Taiwan Univ, Dept Bioenvironm Syst Engn, Taipei 10770, Taiwan

[2] Natl Taiwan Univ, Hydrotech Res Inst, Taipei 10770, Taiwan

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2001年 / 31卷 / 04期

关键词：

flood flow; hydrological processes; nonlinear; radial basis function neural network (RBF NN); rainfall-runoff;

D O I：

10.1109/5326.983936

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A radial basis function (RBF) neural network (NN) is proposed to develop a rainfall-runoff model for three-hour-ahead flood forecasting. For faster training speed, the RBF NN employs a hybrid two-stage learning scheme. During the first stage, unsupervised learning, fuzzy min-max clustering is introduced to determine the characteristics of the nonlinear RBFs. In the second stage, supervised learning, multivariate linear regression is used to determine the weights between the hidden and output layers. The rainfall-runoff relation can be considered as a linear combination of some nonlinear RBFs. Rainfall and runoff events of the Lanyoung River collected during typhoons are used to train, validate,and test the network. The results show that the RBF NN can be considered as a suitable technique for predicting flood flow.

引用

页码：530 / 535

页数：6

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