A Study of Combined OLS with SGA to Construct RBF Neural Networks for Flood Forecasting
Date Issued
2004
Date
2004
Author(s)
Lin, Yung-Tang
DOI
zh-TW
Abstract
Artificial neural network (ANN) is a state of arts technique that is capable of identifying complex non-linear relationships between input and output data without understanding the mechanisms of system. In this study, the Radial basis function neural network (RBFNN) is used to model the rainfall-runoff process. The training process in RBFNN includes two phases. In the first phase, Orthogonal Least Squares (OLS) is used to determine the number and the center of radial basis function in the hidden layer. In the second phase, the parameters in radial basis functions and the connected weights between the hidden layer and output layer are determined by Stochastic Gradient Approach (SGA). The OLS algorithm can systematically identify effective input data and then set them to be the nodes of hidden layer, so that the time-consuming trial-and-error procedure can be relieved. In the second phase, SGA algorithm is used to search optimal parameters of the network. The proposed RBFNN is first verified by using a theoretical Fourier function and a chaotic time series. The results show that the model has great ability and high accuracy in simulation and/or estimation the theroretical cases. . To further investigate the model’s applicability, the Lan-Young 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 the BPNN. The 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
垂直最小平方法
類神經網路
序率坡降法
倒傳遞類神經網路
輻狀基底函數類神經網路
降雨－逕流模式
Radial basis function neural network
Back propagation neural network
Orthogonal Least Squares
Stochastic Gradient Approach
Rainfall-runoff model
Artificial neural network (ANN)
Type
thesis
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