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Sustainability 2018, 10(3), 750; https://doi.org/10.3390/su10030750

Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems

Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea
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Author to whom correspondence should be addressed.
Received: 2 February 2018 / Revised: 4 March 2018 / Accepted: 5 March 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Urban Water Management)
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Abstract

Leaks in a water distribution network (WDS) constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA) and artificial neural network (ANN) were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems. View Full-Text
Keywords: artificial neural network; leakage ratio; principal component analysis; Z-score; water distribution systems artificial neural network; leakage ratio; principal component analysis; Z-score; water distribution systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Jang, D.; Park, H.; Choi, G. Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems. Sustainability 2018, 10, 750.

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