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

Dongwoo Jang, Hyoseon Park and Gyewoon Choi
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Dongwoo Jang: Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea
Hyoseon Park: Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea
Gyewoon Choi: Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea

Sustainability, 2018, vol. 10, issue 3, 1-13

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.

Keywords: artificial neural network; leakage ratio; principal component analysis; Z-score; water distribution systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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