This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input vari- ables for LSSVM model and the LSSVM model which works as time series forecasting. 5 In this study the application of GLSSVM for monthly river fl ow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artifi cial neural network (ANN) models, Autoregres- sive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river fl ow discharge. The standard statistical, the 10 root mean square error (RMSE) and coe ffi cient of correlation (R) are employed to eval- uate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to mo

important A hybrid least squares support vector.pdf

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