[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118127-en":3,"doc-seo-118127-105":30,"detail-sidebar-cat-0-en-105":91},{"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},118127,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Leak and Burst Detection in Water Distribution Network Using Logic-and Machine Learning-Based Approaches","Urban water systems face two pressing pressures: declining water resources and aging infrastructure, making leak reduction essential. Traditional leak and burst detection methods often yield late recognition, longer leak durations, and higher maintenance expenses. This study evaluates logic-and machine learning-based techniques for early detection and accurate localization in water distribution networks by combining sensor hardware, data analysis, and algorithm design. Using SCADA data from a case area in Sunbury (Victoria, Australia), logic rules and multiple machine learning models are compared, with the Local Outlier Factor selected for superior anomaly detection. A web-based platform is implemented for deployment, supporting future real-time operations and more proactive maintenance.","Leak and Burst Detection in Water Distribution Network Using Logic-and Machine Learning-Based Approaches  \nThis is the Published version of the following publication  \nSharma, Ashok, Joseph, Kiran, Shetty, Jyoti, Van Staden, Rudi, Wasantha, PLP, Small, Sharna and Burnett, Nathan (2024) Leak and Burst Detection in Water Distribution Network Using Logic-and Machine Learning-Based Approaches. Water, 16 (14) . pp. 1-21. ISSN 2073-4441  \nThe publisher’s official version can be found at [https://www.mdpi.com/2073-4441/16/14/1935](https://www.mdpi.com/2073-4441/16/14/1935)[ ](https://www.mdpi.com/2073-4441/16/14/1935)Note that access to this version may require subscription.  \nDownloaded from VU Research Repository [https://vuir.vu.edu.au/48375/](https://vuir.vu.edu.au/48375/)  \n water   \nArticle  \nLeak and Burst Detection in Water Distribution Network Using Logic-and Machine Learning-Based Approaches  \nKiran Joseph 1,2, Jyoti Shetty 3, Ashok K. Sharma 1, *, Rudi van Staden 1, P. L. P. Wasantha 1, Sharna Small 2 and Nathan Bennett 2  \nCitation: Joseph, K.; Shetty, J.; Sharma, A.K.; van Staden, R.; Wasantha, P.L.P.; Small, S.; Bennett, N. Leak and Burst Detection in Water Distribution Network Using Logic-and Machine Learning-Based Approaches. Water 2024, 16, 1935. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/w16141935](10.3390/w16141935)  \nAcademic Editors: Christos S. Akratos, Husnain Haider and Haroon R. Mian  \nReceived: 9 May 2024  \nRevised: 22 June 2024  \nAccepted: 4 July 2024  \nPublished: 9 July 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 Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 3011, Australia; [kiran.joseph2@live.vu.edu.au](kiran.joseph2@live.vu.edu.au) (K.J.); [rudi.vanstaden@vu.edu.au](rudi.vanstaden@vu.edu.au) (R.v.S.); [wasantha.pallewelaliyanage@vu.edu.au](wasantha.pallewelaliyanage@vu.edu.au) (P.L.P.W.)  \n2 Greater Western Water, Melbourne, VIC 3429, Australia; [nathan.bennett@gww.com.au](nathan.bennett@gww.com.au) (N.B.); [misharna.small@gww.com.au](misharna.small@gww.com.au) (S.S.)  \n3 Department of Computer Science and Engineering, RV College of Engineering, Mysore Road, Bengaluru 560059, India; [jyothis@rvce.edu.in](jyothis@rvce.edu.in)  \n* Correspondence: [ashok.sharma@vu.edu.au](ashok.sharma@vu.edu.au)  \nAbstract: Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logicand machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings under","cbCaigsVQ7Wsuvae","https://ap.wps.com/l/cbCaigsVQ7Wsuvae","pdf",5181993,1,22,"English","en",105,"# Abstract\n# Problem Background: Water loss from leakage\n## Limitations of conventional detection methods\n# Proposed Approach: Logic-and machine learning integration\n## Sensor and data pipeline\n## Logic-based algorithms for anomaly conditions\n## Machine learning models and comparative evaluation\n# Case Study: Sunbury region pumping main with SCADA\n## Extracting hydraulic characteristics from SCADA data\n# Model Selection and Deployment\n## Local Outlier Factor (LOF)\n## Web-based detection platform\n# Findings and Implications for real-time monitoring","[{\"question\":\"What challenge does this research address in urban water systems?\",\"answer\":\"The study targets minimizing water losses caused by leaks and bursts, especially when conventional detection methods detect problems too late.\"},{\"question\":\"How do the logic-based and machine learning approaches work together?\",\"answer\":\"Logic-based algorithms capture anomalies using predefined conditions, while machine learning models learn from historical data to detect complex, evolving patterns and outliers.\"},{\"question\":\"Which machine learning model is selected and why?\",\"answer\":\"The Local Outlier Factor (LOF) is selected because comparative analysis shows it performs best for anomaly detection in the evaluated scenario.\"}]","Leak and Burst Detection in Water Distribution Network Using Logic-and Machine Learning-Based Approaches | 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