Model application for monitoring and locating leakages in rural area water pipeline networks

Xiaoqin Li, Yannan Jia, Dan Zhang, Jifu Yang and Zheng Chen

Water International, 2023, vol. 48, issue 3, 309-321

Abstract: Monitoring and locating leaks in water supply pipelines are critical to the safety of rural drinking water, which is a highlighted issue in China. To meet this need, an XGBoost-based model was developed and applied to the rural water supply network in Dingyuan, China. It could diagnose water leakage while overcoming the obstacles caused by the limited scale and incompleteness of data. In a comparative case study, the proposed model outperformed the probabilistic neural network models, which require large-scale data, in terms of both F1-score and accuracy, thus demonstrating its capability to accurately locate leakage in rural water supply pipelines.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02508060.2023.2195722 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:rwinxx:v:48:y:2023:i:3:p:309-321

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rwin20

DOI: 10.1080/02508060.2023.2195722

Access Statistics for this article

Water International is currently edited by James Nickum, Philippus Wester, Remy Kinna, Xueliang Cai, Yoram Eckstein, Naho Mirumachi and Cecilia Tortajada

More articles in Water International from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().