Building Radial Basic Function Neural Network by Integrating OLS and SGA for Flood Forecasting
Resource
臺灣水利,53(4),25-38.
Journal
臺灣水利
Journal Volume
53
Journal Issue
4
Pages
25-38
Date Issued
2005-12
Date
2005-12
Author(s)
Abstract
In this study, the radial basis function neural network (RBFNN) is used to model the rainfall-runoff process. The training process of RBFNN includes two phases. First, the Orthogonal Least Squares (OLS) is used to determine the number and the center of radial basis function in the hidden layer. Then, the parameters in radial basis functions and the connected weights between the hidden layer and output layer are determined by the Stochastic Gradient Approach (SGA). The OLS algorithm could systematically identify effective input data and set them as the nodes of hidden layer, while the SGA algorithm could search optimal parameters of the network. The proposed RBFNN is first verified by using a theoretical Fourier function. The results show that the model has great ability and high accuracy in simulation of the theoretical case. To further investigate the models' sapplicability, Lanyang River is used as case study. The Back propagation neural network (BPNN) is also performed for the purpose of comparison. The results demonstrate that the proposed RBFNN has much better performance than BPNN. RBFNN not only provides an efficient way to model the rainfall-runoff process, but also give precise one-step and two-step ahead flood forecasts.
Subjects
Rainfall-runoff model
Radial basis function neural network
Orthogonal least squares
Stochastic gradient approach
Back propagation neural network
Type
journal article
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