Mounce, S.R., Pedraza, C., Jackson, T. et al. (2 more authors) (2015) Cloud based machine learning approaches for leakage assessment and management in smart water networks. In: Procedia Engineering. , 02-04 Sep 2015, De Montfort University, Leicester, UK. Elsevier , pp. 43-52. ISBN 1877-7058
Abstract
One-third of utilities around the globe report a loss of more than 40 percent of clean water due to leaks. By reducing the amount of water leaked, smart water networks can help reduce the money wasted on producing or purchasing water, and the related energy required to pump water and treat water for distribution. A UK demo site is presented focusing on leak management, integrating fixed flow and pressure instrumentation, advanced (smart) metering infrastructure and novel instruments (capable of high resolution monitoring). Example data analysis results for this site using the AURA-Alert anomaly detection system for Condition Monitoring are presented.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2015 Mounce et al. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Scientific Committee |
Keywords: | Leakage; Smart Networks; AMR; Neural Networks; Cloud computing |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Jan 2016 15:07 |
Last Modified: | 19 Dec 2022 13:32 |
Published Version: | http://dx.doi.org/10.1016/j.proeng.2015.08.851 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:94029 |