Using support vector machines for long-term discharge prediction

Jian Yi Lin, Chun Tian Cheng, Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

486 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)599-612
Number of pages14
JournalHydrological Sciences Journal
Issue number4
Publication statusPublished - 1 Aug 2006


  • Autoregressive moving-average (ARMA) models
  • Long-term discharge prediction
  • Neural networks
  • SCE-UA algorithm
  • Support vector machine

ASJC Scopus subject areas

  • Water Science and Technology


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