Universiti Teknologi Malaysia Institutional Repository

A hybrid least squares support vector machines and GMDH approach for river flow forecasting

Samsudin, Ruhaidah and Saad, Puteh and Shabri, Ani (2010) A hybrid least squares support vector machines and GMDH approach for river flow forecasting. Hydrology and Earth System Sciences Discussions, 7 (3). 3691 - 3731.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.5194/hessd-7-3691-2010

Abstract

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 variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

Item Type:Article
Uncontrolled Keywords:kiv record
Subjects:Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:22798
Deposited By: Kamariah Mohamed Jong
Deposited On:30 Aug 2017 07:21
Last Modified:13 Oct 2017 12:08

Repository Staff Only: item control page