A Non-linear Rainfall-runoff Model Using Radial Basis Function Network
Resource
Journal of Hydrology 289 (4): 1-8
Journal
Journal of Hydrology
Journal Volume
289
Journal Issue
4
Pages
1-8
Date Issued
2004
Date
2004
Author(s)
Lin, G.F.
Abstract
In this paper, the radial basis function network (RBFN) is used to construct a rainfall-runoff model, and the fully supervised learning algorithm is presented for the parametric estimation of the network. The fully supervised learning algorithm has advantages over the hybrid-learning algorithm that is less convenient for setting up the number of hidden layer neurons. The number of hidden layer neurons can be automatically constructed and the training error then decreases with increasing number of neurons. The early stopping technique that can avoid over-fitting is adopted to cease the training during the process of network construction. The proposed methodology is finally applied to an actual reservoir watershed to find the one- to three-hour ahead forecasts of inflow. The result shows that the RBFN can be successfully applied to build the relation of rainfall and runoff. © 2004 Elsevier B.V. All rights reserved.
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Type
journal article
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