Flood Forecasting Using Radial Basis Function Neural Networks
Resource
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 31 (4): 530-535
Journal
IEEE Transactions on Systems, Man, and Cybernetics
Pages
-
Date Issued
2001
Date
2001
Author(s)
Abstract
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.
SDGs
Type
journal article
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