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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 11
Hydrol. Earth Syst. Sci., 16, 4417–4433, 2012
https://doi.org/10.5194/hess-16-4417-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 16, 4417–4433, 2012
https://doi.org/10.5194/hess-16-4417-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Nov 2012

26 Nov 2012

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

S. Ismail1, A. Shabri2, and R. Samsudin S. Ismail et al.
  • 1Department of Mathematics, Science Faculty, Universiti Teknologi Malaysia, Malaysia
  • 2Department of Software Engineering, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Malaysia

Abstract. Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM model for river flow forecasting. The hybrid model uses the SOM algorithm to cluster the entire dataset into several disjointed clusters, where the monthly river flows data with similar input pattern are grouped together from a high dimensional input space onto a low dimensional output layer. By doing this, the data with similar input patterns will be mapped to neighbouring neurons in the SOM's output layer. After the dataset has been decomposed into several disjointed clusters, an individual LSSVM is applied to forecast the river flow. The feasibility of this proposed model is evaluated with respect to the actual river flow data from the Bernam River located in Selangor, Malaysia. The performance of the SOM-LSSVM was compared with other single models such as ARIMA, ANN and LSSVM. The performance of these models was then evaluated using various performance indicators. The experimental results show that the SOM-LSSVM model outperforms the other models and performs better than ANN, LSSVM as well as ARIMA for river flow forecasting. It also indicates that the proposed model can forecast more precisely, and provides a promising alternative technique for river flow forecasting.

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