Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28016
Title: Using support vector machines for long-term discharge prediction
Authors: Lin, JY
Cheng, CT
Chau, KW 
Keywords: Autoregressive moving-average (ARMA) models
Long-term discharge prediction
Neural networks
SCE-UA algorithm
Support vector machine
Issue Date: 2006
Publisher: Iahs Press, Inst Hydrology
Source: Hydrological sciences journal, 2006, v. 51, no. 4, p. 599-612 How to cite?
Journal: Hydrological Sciences Journal 
Abstract: Accurate time- and site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive moving-average (ARMA) models have been used in modelling water resource time series as a standard representation of stochastic time series. Recently, artificial neural network (ANN) approaches have been proven to be efficient when applied to hydrological prediction. In this paper, the support vector machine (SVM) is presented as a promising method for hydrological prediction. Over-fitting and local optimal solution are unlikely to occur with SVM, which implements the structural risk minimization principle rather than the empirical risk minimization principle. In order to identify appropriate parameters of the SVM prediction model, a shuffled complex evolution algorithm is performed through exponential transformation. The SVM prediction model is tested using the long-term observations of discharges of monthly river flow discharges in the Manwan Hydropower Scheme. Through the comparison of its performance with those of the ARMA and ANN models, it is demonstrated that SVM is a very potential candidate for the prediction of long-term discharges.
URI: http://hdl.handle.net/10397/28016
ISSN: 0262-6667
DOI: 10.1623/hysj.51.4.599
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