Husin, Abdullah and Ku-Mahamud, Ku Ruhana (2017) Reservoir gate opening classification using multiple classifier system with ant system-based feature decomposition. In: 6th International Conference on Computing & Informatics (ICOCI2017), 25 - 27 April 2017, Kuala Lumpur.

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Abstract

Classification of reservoir gate opening (RGO) is an important task in flood management.Reservoir water level has been used to determine the number of gates to be opened when flood is imminent to prevent disaster.Predicting the number of gates to be opened is crucial to avoid any disaster. Multiple classifier system has been shown to provide better classification accuracy as compared to single classifier system.However, there is no guideline on the number of classifiers to be combined and no measurement was proposed to measure the compactness of the classifiers.This study proposes an ant system-based feature decomposition approach to develop a multiple classifier ensemble for classification of RGO.Experiments have been conducted using the k-nearest neighbour, decision tree, nearest mean classifier and linear discriminant analysis as base classifier, and performance of ant system has been compared with random subspace method.Based on the results, it can be concluded that the multiple classifier with ant system-based feature decomposition produced better classification accuracy than random subspace method. Best classification results were obtained when multiple decision tree is constructed to make predictions of RGO with an average accuracy of 89.17%. This method is expected to be useful to apply for RGO classification and future work can be done to include rainfall precipitation besides reservoir water level.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	eISSN 2289-7402 e-ISBN 978-967-0910-33-8 Organized by: School of Computing, Universiti Utara Malaysia Sintok
Uncontrolled Keywords:	reservoir gate opening, multiple classifier system, ant system
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Prof. Dr. Ku Ruhana Ku Mahamud
Date Deposited:	26 Jul 2017 07:55
Last Modified:	26 Jul 2017 07:55
URI:	https://repo.uum.edu.my/id/eprint/22805

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