Daniel Dopazo Daniel.Dopazo@uwe.ac.uk
Research Fellow- Data Science
Daniel Dopazo Daniel.Dopazo@uwe.ac.uk
Research Fellow- Data Science
Richard Everson
Sarah Rogerson
Raziyeh Farmani
Savic Dragan
The leak repair process is one of the costliest operations for the water supply companies due to the amount of water lost, the customer inconvenience, and the further damages made to the infrastructure. To tackle this problem, a new approach is presented for detecting and prioritizing leaks at a district metered area (DMA) level. The performance of the algorithm is based on the extraction of the most meaningful features of the water flow daily, and the solution predicts the minimum night flow using neighbor DMA data to analyze and filter the noise for detecting and prioritizing leakages. The purposed solution has been validated with real historical data using a subset of 171 similar DMAs with data coming from January 2016 to July 2020. The algorithm was able to detect leakages successfully in nearly 72% of the cases, making around 18% of false positives. In conclusion, the presented approach consists of a full-fledged methodology able to predict the minimum night flow, detect leakages reliably, and prioritize them at a DMA level efficiently and reliably.
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | World Environmental and Water Resources Congress 2021: Planning a Resilient Future along America's Freshwaters - Selected Papers from the World Environmental and Water Resources Congress 2021 |
Start Date | Jun 7, 2021 |
End Date | Jun 11, 2021 |
Acceptance Date | Aug 1, 2021 |
Online Publication Date | Jun 3, 2021 |
Publication Date | Jun 3, 2021 |
Deposit Date | Aug 22, 2022 |
Publisher | American Society of Civil Engineers |
Pages | 1023-1032 |
Book Title | World Environmental and Water Resources Congress 2021 |
Chapter Number | Water distribution systems analysis symposium |
ISBN | 9780784483466 |
DOI | https://doi.org/10.1061/9780784483466.095 |
Keywords | Water conservation, Client relationships, Water meters, Water supply systems, System analysis, Water supply, Algorithms, Water leakage and water loss |
Public URL | https://uwe-repository.worktribe.com/output/9852451 |
Publisher URL | https://ascelibrary.org/doi/10.1061/9780784483466.095 |
Related Public URLs | https://ascelibrary.org/doi/book/10.1061/9780784483466 |
A leakage detection system extracting the most meaningful features with decision trees
(2020)
Presentation / Conference Contribution
Assessing movement quality on straight leg raise using neural networks and data science
(2022)
Journal Article
An automated machine learning approach for classifying infrastructure cost data
(2023)
Journal Article
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search