In most city water distribution systems, a considerable amount of water is lost because of leaks occurring in pipes. Moreover, an unobservable fluid leakage fault that may occur in a hazardous industrial system, such as nuclear power plant cooling process or chemical waste disposal, can cause both environmental and economical disasters. This situation generates crucial interest for industry and academia due to the financial cost related with public health risks, environmental responsibility, and energy efficiency. In this paper, to find a reliable and economic solution for this problem, adaptive neuro fuzzy inference system (ANFIS) method which consists of backpropagation and least-squares learning algorithms is proposed for estimating leakage locations in a complex water distribution system. The hybrid algorithm is trained with acceleration, pressure, and flow rate data measured through the sensors located on some specific points of the complex water distribution system. The effectiveness of the proposed method is discussed comparing the results with the current methods popularly used in this area.
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December 2018
Research-Article
Water Leakage Detection for Complex Pipe Systems Using Hybrid Learning Algorithm Based on ANFIS Method
Barış Can Yalçın,
Barış Can Yalçın
Mechatronics Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: bcyalcin@yildiz.edu.tr
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: bcyalcin@yildiz.edu.tr
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Cihan Demir,
Cihan Demir
Mechanical Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: cdemir@yildiz.edu.tr
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: cdemir@yildiz.edu.tr
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Murat Gökçe,
Murat Gökçe
İstanbul Water and Sewerage Administration,
Kağıthane, İstanbul 34406, Turkey
e-mail: 1muratgkc@gmail.com
Kağıthane, İstanbul 34406, Turkey
e-mail: 1muratgkc@gmail.com
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Ahmet Koyun
Ahmet Koyun
Mechatronics Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: koyun@yildiz.edu.tr
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: koyun@yildiz.edu.tr
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Barış Can Yalçın
Mechatronics Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: bcyalcin@yildiz.edu.tr
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: bcyalcin@yildiz.edu.tr
Cihan Demir
Mechanical Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: cdemir@yildiz.edu.tr
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: cdemir@yildiz.edu.tr
Murat Gökçe
İstanbul Water and Sewerage Administration,
Kağıthane, İstanbul 34406, Turkey
e-mail: 1muratgkc@gmail.com
Kağıthane, İstanbul 34406, Turkey
e-mail: 1muratgkc@gmail.com
Ahmet Koyun
Mechatronics Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: koyun@yildiz.edu.tr
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: koyun@yildiz.edu.tr
1Corresponding author.
Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received November 17, 2017; final manuscript received April 19, 2018; published online July 3, 2018. Assoc. Editor: John Michopoulos.
J. Comput. Inf. Sci. Eng. Dec 2018, 18(4): 041004 (10 pages)
Published Online: July 3, 2018
Article history
Received:
November 17, 2017
Revised:
April 19, 2018
Citation
Yalçın, B. C., Demir, C., Gökçe, M., and Koyun, A. (July 3, 2018). "Water Leakage Detection for Complex Pipe Systems Using Hybrid Learning Algorithm Based on ANFIS Method." ASME. J. Comput. Inf. Sci. Eng. December 2018; 18(4): 041004. https://doi.org/10.1115/1.4040130
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