Flood forecasting using radial basis function neural networks

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 introd...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2001-11, Vol.31 (4), p.530-535
Main Authors: Chang, F.-J., Jin-Ming Liang, Yen-Chang Chen
Format: Article
Language:English
Subjects:
Flood forecasting
Floods
Fuzzy
Learning
Linear regression
Neural networks
Nonlinearity
Predictive models
Radial basis function
Radial basis function networks
Rivers
Supervised learning
Testing
Trains
Typhoons
Unsupervised learning
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Summary: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 a suitable technique for predicting flood flow.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/5326.983936