Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks
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 pi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/13/13/1841 |
| _version_ | 1797527839274172416 |
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| author | Miguel Capelo Bruno Brentan Laura Monteiro Dídia Covas |
| author_facet | Miguel Capelo Bruno Brentan Laura Monteiro Dídia Covas |
| author_sort | Miguel Capelo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:48:32Z |
| format | Article |
| id | doaj.art-0a6dff1215024f12827b0a939608e6f9 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T09:48:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-0a6dff1215024f12827b0a939608e6f92023-11-22T02:55:06ZengMDPI AGWater2073-44412021-07-011313184110.3390/w13131841Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural NetworksMiguel Capelo0Bruno Brentan1Laura Monteiro2Dídia Covas3CERIS, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalHydraulic Engineering and Water Resources Department, School of Engineering, Federal University of Minas Gerais, Belo Horizonte 31270-901, BrazilCERIS, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalCERIS, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalThe 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.https://www.mdpi.com/2073-4441/13/13/1841burst locationburst quantificationwater distribution networksArtificial Neural Networks |
| spellingShingle | Miguel Capelo Bruno Brentan Laura Monteiro Dídia Covas Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks Water burst location burst quantification water distribution networks Artificial Neural Networks |
| title | Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks |
| title_full | Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks |
| title_fullStr | Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks |
| title_full_unstemmed | Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks |
| title_short | Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks |
| title_sort | near real time burst location and sizing in water distribution systems using artificial neural networks |
| topic | burst location burst quantification water distribution networks Artificial Neural Networks |
| url | https://www.mdpi.com/2073-4441/13/13/1841 |
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