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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 6
Hydrol. Earth Syst. Sci., 15, 1835–1852, 2011
https://doi.org/10.5194/hess-15-1835-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 15, 1835–1852, 2011
https://doi.org/10.5194/hess-15-1835-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  17 Jun 2011

17 Jun 2011

River flow time series using least squares support vector machines

R. Samsudin1, P. Saad1, and A. Shabri2 R. Samsudin et al.
  • 1Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
  • 2Faculty of Science, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

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