Understanding district metered area level leakage using explainable machine learning
dc.contributor.author | Hayslep, M | |
dc.contributor.author | Keedwell, E | |
dc.contributor.author | Farmani, R | |
dc.contributor.author | Pocock, J | |
dc.date.accessioned | 2023-05-23T13:44:01Z | |
dc.date.issued | 2023-01-01 | |
dc.date.updated | 2023-05-23T13:13:32Z | |
dc.description.abstract | Understanding the various interrelated effects that result in leakage is vital to the effort to reduce it. This paper aims to understand, at the district metered area (DMA) level, the relationship between leakage and static characteristics of a DMA, i.e. without considering pressure or flow. The characteristics used include the number of pipes and connections, total DMA volume and network density, as well as pipe diameter, length, age, and material statistics. Leakage, especially background and unreported leakage, can be difficult to accurately quantify. Here, the Average Weekly Minimum Night Flow (AWM) over the last 5 years is used as a proxy for leakage. While this may include some legitimate demand, it is generally assumed that minimum night flow, strongly correlates with leakage. A data-driven case study on over 800 real DMAs from UK networks is conducted. Two regression models, a decision tree model and an elastic net linear regression model, are created to predict the AWM of unseen DMAs. Reasonable accuracy was achieved, considering pressure is not an included feature, and the models are investigated for the most prominent features related to leakage. | en_GB |
dc.description.sponsorship | South West Water (SWW) | en_GB |
dc.identifier.citation | Vol. 1136, article 012040 | en_GB |
dc.identifier.doi | https://doi.org/10.1088/1755-1315/1136/1/012040 | |
dc.identifier.uri | http://hdl.handle.net/10871/133220 | |
dc.identifier | ORCID: 0000-0002-0767-0619 (Hayslep, Matthew) | |
dc.identifier | ORCID: 0000-0003-3650-6487 (Keedwell, Edward) | |
dc.identifier | ORCID: 0000-0001-8148-0488 (Farmani, Raziyeh) | |
dc.language.iso | en | en_GB |
dc.publisher | IOP Publishing | en_GB |
dc.rights | © 2023. Published under licence by IOP Publishing Ltd. Open access. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | en_GB |
dc.title | Understanding district metered area level leakage using explainable machine learning | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-05-23T13:44:01Z | |
dc.identifier.issn | 1755-1307 | |
dc.description | This is the final version. Available on open access from IOP Publishing via the DOI in this record | en_GB |
dc.description | 14th International Conference on Hydroinformatics, 4 - 8 July 2022, Bucharest, Romania | en_GB |
dc.identifier.eissn | 1755-1315 | |
dc.identifier.journal | IOP Conference Series: Earth and Environmental Science | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2023-01-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-05-23T13:39:26Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2023-05-23T13:44:05Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2023. Published under licence by IOP Publishing Ltd. Open access. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.