Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks
1
CERIS, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
2
Hydraulic Engineering and Water Resources Department, School of Engineering, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Stefano Alvisi
Water 2021, 13(13), 1841; https://doi.org/10.3390/w13131841
Received: 25 May 2021
/
Revised: 17 June 2021
/
Accepted: 28 June 2021
/
Published: 1 July 2021
(This article belongs to the Special Issue Water Supply Assessment Systems Developing)
The current paper proposes a novel methodology for near–real time burst location and sizing in water distribution systems (WDS) by means of Multi–Layer Perceptron (MLP), a class of artificial neural network (ANN). The proposed methodology can be systematized in four steps: (1) construction of the pipe–burst database, (2) problem formulation and ANN architecture definition, (3) ANN training, testing and sensitivity analyses, (4) application based on collected data. A large database needs to be constructed using 24 h pressure–head data collected or numerically generated at different sensor locations during the pipe burst occurrence. The ANN is trained and tested in a real–life network, in Portugal, using artificial data generated by hydraulic extended period simulations. The trained ANN has demonstrated to successfully locate 60–70% of the burst with an accuracy of 100 m and 98% of the burst with an accuracy of 500 m and to determine burst sizes with uncertainties lower than 2 L/s in 90% of tested cases and lower than 0.2 L/s in 70% of the cases. This approach can be used as a daily management tool of water distribution networks (WDN), as long as the ANN is trained with artificial data generated by an accurate and calibrated WDS hydraulic models and/or with reliable pressure–head data collected at different locations of the WDS during the pipe burst occurrence.
View Full-Text
Keywords:
burst location; burst quantification; water distribution networks; Artificial Neural Networks
▼
Show Figures
Figure 1
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.
MDPI and ACS Style
Capelo, M.; Brentan, B.; Monteiro, L.; Covas, D. Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks. Water 2021, 13, 1841. https://doi.org/10.3390/w13131841
AMA Style
Capelo M, Brentan B, Monteiro L, Covas D. Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks. Water. 2021; 13(13):1841. https://doi.org/10.3390/w13131841
Chicago/Turabian StyleCapelo, Miguel, Bruno Brentan, Laura Monteiro, and Dídia Covas. 2021. "Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks" Water 13, no. 13: 1841. https://doi.org/10.3390/w13131841
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
