[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124367-en":3,"doc-seo-124367-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124367,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Attributing Minimum Night Flow to Individual Pipes in Real-World Water Distribution Networks Using Machine Learning - Proceeding Paper","This article introduces an explainable machine learning approach to estimate how much flow each pipe contributes to the minimum night flow (MNF) within a district metered area (DMA). The method is validated using MNF observations from multiple DMAs and records of pipe failures, demonstrating strong performance across both evaluation tasks. Predicted pipe-level MNF can support leak management and intervention planning. Training, validation, and testing use 800 UK DMAs covering nearly 12 million meters of pipe.","Proceeding Paper  \nAttributing Minimum Night Flow to Individual Pipes in Real-World Water Distribution Networks Using Machine Learning †  \nMatthew Hayslep 1, *, Edward Keedwell 1, Raziyeh Farmani 2 and Joshua Pocock 3  \nCitation: Hayslep, M.; Keedwell, E.; Farmani, R.; Pocock, J. Attributing Minimum Night Flow to Individual Pipes in Real-World Water Distribution Networks Using Machine Learning. Eng. Proc. 2024, 69, 112 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)engproc2024069112  \nAcademic Editors: Stefano Alvisi, Marco Franchini, Valentina Marsiliand Filippo Mazzoni  \nPublished: 10 September 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK; [e.c.keedwell@exeter.ac.uk](e.c.keedwell@exeter.ac.uk)  \n2 Department of Engineering, University of Exeter, Exeter EX4 4QF, UK; [r.farmani@exeter.ac.uk](r.farmani@exeter.ac.uk)  \n3 South West Water, Exeter EX2 7HR, UK; [jpocock@southwestwater.co.uk](jpocock@southwestwater.co.uk)  \n* [Correspondence: mh989@exeter.ac.uk](Correspondence: mh989@exeter.ac.uk)  \n† Presented at the 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024), Ferrara, Italy, 1–4 July 2024 .  \nAbstract: This article introduces an explainable machine learning model for estimating the amount of flow that each pipe in a district metered area (DMA) contributes to the minimum night flow (MNF) . This approach is validated using the MNF of DMAs and pipe failures, showing good results for both tasks. The predictions from this model could be used to guide leak management or intervention strategies. In total, 800 DMAs ranging from rural to urban networks and representing nearly 12 million meters of pipe from a UK water company are used to train, validate, test, and evaluate the methodology.  \nKeywords: machine learning; minimum night flow; water distribution network; leakage; pipe failure  \n1. Introduction  \nMinimum night flow (MNF) is an important metric commonly used to estimate and understand leakage [1] within district meter areas (DMAs) and is the most common leakage assessment methodology used in the UK [2] . Leakage is a pervasive problem with economic and environmental consequences [3], making it important for water companies, regulators, and governments. One of the main drawbacks to MNF is the resolution of data; even with the complete coverage of smart meters, it is difficult to attribute the remaining water balance to particular pipes. An alternative way to understand leakage is to study known cases of historic leaks and bursts [3], i.e., pipe failures. This relies on the records of utility companies regarding engineering work conducted on the infrastructure. One of the downsides of using historic pipe failures to understand leakage is that this will not include unreported or background leakage (unlike MNF) . MNF approximates leakage under the assumption that legitimate water usage is lowest at night, and therefore that most of the flow during this period is leakage.  \nPrevious studies have used machine learning methods to estimate the MNF of entire DMAs based on various factors such as total customers, total pipe length, etc. [4,5] . This paper differs from these previous works by predicting the contribution of individual pipes to MNF. Furthermore, this approach uses data from 800 real-world DMAs that are readily available to water companies, making it applicable to a wide range of real-world scenarios. Finally, by attributing MNF to particular pipes in an explainable manner, this methodology provides more infor","cbCainL3eRnObv2R","https://ap.wps.com/l/cbCainL3eRnObv2R","pdf",737599,1,4,"English","en",105,"# Introduction\n## MNF as a leakage assessment metric\n## Limitations of MNF data resolution\n## Prior work and research gap\n# Materials and Methods\n## Pipe-level MNF (pipe-MNF) linear regression model\n## Features and data sources\n## Dataset splitting and validation strategy","[{\"question\":\"What problem does the paper address in water distribution networks?\",\"answer\":\"The paper addresses the difficulty of attributing minimum night flow (MNF) to specific pipes, which limits traditional leakage assessment resolution within district metered areas (DMAs).\"},{\"question\":\"How does the proposed model estimate pipe-level minimum night flow?\",\"answer\":\"It uses a linear regression model that predicts the amount of MNF associated with each pipe based on pipe asset features (e.g., diameter, age, material categories, and connection counts) and recorded pipe failures mapped to the closest pipe.\"},{\"question\":\"What data and scale support the model’s validation?\",\"answer\":\"The study trains and evaluates the approach using data from 800 real-world UK DMAs, representing nearly 12 million meters of pipes, and validates predictions using both MNF measurements and pipe failure information.\"}]","Attributing Minimum Night Flow to Individual Pipes in Real-World Water Distribution Networks Using Machine Learning - Proceeding Paper | PDF",1785821847,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"attributing-minimum-night-flow-to-individual-pipes-in-real-world-water-distribution-networks-using-machine-learning-proceeding-paper","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/attributing-minimum-night-flow-to-individual-pipes-in-real-world-water-distribution-networks-using-machine-learning-proceeding-paper/124367/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the paper address in water distribution networks?","Question",{"text":74,"@type":75},"The paper addresses the difficulty of attributing minimum night flow (MNF) to specific pipes, which limits traditional leakage assessment resolution within district metered areas (DMAs).","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed model estimate pipe-level minimum night flow?",{"text":79,"@type":75},"It uses a linear regression model that predicts the amount of MNF associated with each pipe based on pipe asset features (e.g., diameter, age, material categories, and connection counts) and recorded pipe failures mapped to the closest pipe.",{"name":81,"@type":72,"acceptedAnswer":82},"What data and scale support the model’s validation?",{"text":83,"@type":75},"The study trains and evaluates the approach using data from 800 real-world UK DMAs, representing nearly 12 million meters of pipes, and validates predictions using both MNF measurements and pipe failure information.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]