Modelling of river discharges and rainfall using radial basis function networks based on support vector regression

Associative memory networks (AMNs) based on radial basis functions (RBFs) are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. However, good generalization results can only be obtained if the structure of the RBF network is suitably chosen. An approach to select the structure of the RBF networks based on the support vectors (SVs) of the support vector machine (SVM) has been proposed. The main advantage of this approach is that the structure of the network can be obtained objectively, as the SVs of the SVM are obtained from a constrained optimization for a given error bound. For convenience, this class of AMNs is referred to as support vector radial basis function networks (SVRBFNs). In this paper, the modelling of the relationship between rainfall and river discharges of the Fuji river using the SVRBFN is presented. As there are large outliers in the modelling errors arising from the data collection process, they are removed first before retraining the SVRBFN using the adjusted data, in order to obtain a better approximation of the relationship between rainfall and river discharges. The generalization ability of the SVRBFN is verified using the test data that are the most recent not used in the training of the network. The prediction of river discharges for given rainfalls can be computed from the SVRBFN, which can provide early warning of severe river discharges when there is heavy and prolonged rainfall.