Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22780
Title: Leak detection of pipeline : an integrated approach of rough set theory and artificial bee colony trained SVM
Authors: Mandal, SK
Chan, FTS 
Tiwari, MK
Keywords: Artificial bee colony (ABC) algorithm
Leak detection
Rough set theory
Rule generation
Support vector machine (SVM)
Issue Date: 2012
Publisher: Pergamon Press
Source: Expert systems with applications, 2012, v. 39, no. 3, p. 3071-3080 How to cite?
Journal: Expert systems with applications 
Abstract: The generation of leak along the pipeline carrying crude oils and liquid fuels results enormous financial loss to the industry and also affects the public health. Hence, the leak detection and localization problem has always been a major concern for the companies. In spite of the various techniques developed, accuracy and time involved in the prediction is still a matter of concern. In this paper, a novel leak detection scheme based on rough set theory and support vector machine (SVM) is proposed to overcome the problem of false leak detection. In this approach, 'rough set theory' is explored to reduce the length of experimental data as well as generate rules. It is embedded to enhance the decision making process. Further, SVM classifier is employed to inspect the cases that could not be detected by applied rules. For the computational training of SVM, this paper uses swarm intelligence technique: artificial bee colony (ABC) algorithm, which imitates intelligent food searching behavior of honey bees. The results of proposed leak detection scheme with ABC are compared with those obtained by using particle swarm optimization (PSO) and one of its variants, so-called enhanced particle swarm optimization (EPSO). The experimental results advocate the use of propounded method for detecting leaks with maximum accuracy.
URI: http://hdl.handle.net/10397/22780
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2011.08.170
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