{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:27:05Z","timestamp":1781267225464,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hainan Province Science and Technology Special Fund","award":["ZDYF2023GXJS159"],"award-info":[{"award-number":["ZDYF2023GXJS159"]}]},{"name":"Hainan Province Science and Technology Special Fund","award":["ZDYF2023GXJS168"],"award-info":[{"award-number":["ZDYF2023GXJS168"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Information"],"abstract":"<jats:p>In order to enhance leakage detection accuracy in water distribution networks (WDNs) while reducing sensor deployment costs, an intelligent algorithm for the optimal deployment of water network monitoring sensors based on the automatic labelling and graph neural network (ALGN) was proposed for the optimal deployment of WDN monitoring sensors. The research aims to develop a data-driven, topology-aware sensor deployment strategy that achieves high leakage detection performance with minimal hardware requirements. The methodology consisted of three main steps: first, the dung beetle optimization algorithm (DBO) was employed to automatically determine optimal parameters for the DBSCAN clustering algorithm, which generated initial cluster labels; second, a customized graph neural network architecture was used to perform topology-aware node clustering, integrating network structure information; finally, optimal pressure sensor locations were selected based on minimum distance criteria within identified clusters. The key innovation lies in the integration of metaheuristic optimization with graph-based learning to fully automate the sensor placement process while explicitly incorporating the hydraulic network topology. The proposed approach was validated on real-world WDN infrastructure, demonstrating superior performance with 93% node coverage and 99.77% leakage detection accuracy, surpassing state-of-the-art methods by 2% and 0.7%, respectively. These results indicate that the ALGN framework provides municipal water utilities with a robust, automated solution for designing efficient pressure monitoring systems that balance detection performance with implementation cost.<\/jats:p>","DOI":"10.3390\/info16100837","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T09:12:23Z","timestamp":1759223543000},"page":"837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Intelligent Algorithm for the Optimal Deployment of Water Network Monitoring Sensors Based on Automatic Labelling and Graph Neural Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Guoxin","family":"Shi","sequence":"first","affiliation":[{"name":"Hainan Provincial Key Laboratory of Low-Altitude Intelligent Sensing and Information Processing, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6681-6489","authenticated-orcid":false,"given":"Xianpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Hainan Provincial Key Laboratory of Low-Altitude Intelligent Sensing and Information Processing, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hainan Provincial Key Laboratory of Low-Altitude Intelligent Sensing and Information Processing, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlei","family":"Gao","sequence":"additional","affiliation":[{"name":"Guangdong Water Co., Ltd., Shenzhen 518021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"78846","DOI":"10.1109\/ACCESS.2018.2885444","article-title":"Review of current technologies and proposed intelligent methodologies for water distributed network leakage detection","volume":"6","author":"Chan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.scitotenv.2017.07.072","article-title":"Assessment of the municipal water cycle in China","volume":"607","author":"Wang","year":"2017","journal-title":"Sci. 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