Universiti Teknologi Malaysia Institutional Repository

River flow forecasting: a hybrid model of self organizing maps and least square support vector machine

Samsudin, Ruhaidah and Shabri, Ani and S., Ismail (2010) River flow forecasting: a hybrid model of self organizing maps and least square support vector machine. Hydrology and Earth System Sciences Discussions, 7 (5). pp. 8179-8212. ISSN 1812-2108

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Official URL: http://dx.doi.org/10.5194/hessd-7-8179-2010

Abstract

Successful river flow time series forecasting is a major goal and an essential procedure that is necessary in water resources planning and management. This study introduced a new hybrid model based on a combination of two familiar non-linear method of mathematical modeling: Self Organizing Map (SOM) and Least Square Support Vector Machine (LSSVM) model referred as SOM-LSSVM model. The hybrid model uses the SOM algorithm to cluster the training data into several disjointed clusters and the individual LSSVM is used to forecast the river flow. The feasibility of this proposed model is evaluated to actual river flow data from Bernam River located in Selangor, Malaysia. Their results have been compared to those obtained using LSSVM and artificial neural networks (ANN) models. The experiment results show that the SOM-LSSVM model outperforms other models for forecasting river flow. It also indicates that the proposed model can forecast more precisely and provides a promising alternative technique in river flow forecasting.

Item Type:Article
Uncontrolled Keywords:hybrid model, self organizing maps, vector machine
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:26661
Deposited By: Liza Porijo
Deposited On:18 Jul 2012 03:50
Last Modified:25 Aug 2014 09:18

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