Next Article in Journal
Hydrostatic Densitometer for Monitoring Density in Freshwater to Hypersaline Water Bodies
Next Article in Special Issue
Optimal Number of Pressure Sensors for Real-Time Monitoring of Distribution Networks by Using the Hypervolume Indicator
Previous Article in Journal
Comparison of Empirical and Analytical Solutions for Open-Channel Flow Velocity with Common Grass Species in Taiwan
Previous Article in Special Issue
Evaluation of Dam Water-Supply Capacity in Korea Using the Water-Shortage Index
Article

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 burst location; burst quantification; water distribution networks; Artificial Neural Networks
Show Figures

Figure 1

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 Style

Capelo, 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.

Article Access Map by Country/Region

1
Back to TopTop