A hybrid model-based method for leak detection in large scale water distribution networks

Fereidooni, Zahra, Tahayori, Hooman and Bahadori-Jahromi, Ali ORCID: https://orcid.org/0000-0003-0405-7146 (2020) A hybrid model-based method for leak detection in large scale water distribution networks. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

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Abstract

During the past decades, the problem of finding leaks in Water Distribution Networks (WDN) has been controversy. The quicker detection of leaks prevents water loss and helps avoiding their economic and environmental consequences. On the other hand, increasing the speed of leak detection increases the false leak detection that imposes high costs. In this paper, we propose a real-time hybrid method using AI algorithms and hydraulic relations for detecting and locating leaks and identifying the volume of losses material. The proposed method relies on simple and cost-effective flow sensors installed on each junction in the pipeline network. We demonstrate how influential features for leak detection would be generated by using hydraulic equations like Hazen-Williams, Darcy-Weisbach and pressure drop. Through exploiting Decision Tree, KNN, random forest, and Bayesian network we build predictive models and based on the pipeline topology, we locate leaks and their pressure. Comparing the results of applying the proposed method on various leak scenarios shows that the proposed method in this paper, outperforms other existing methods.

Item Type: Article
Identifier: 10.1007/s12652-020-02233-2
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in the Journal of Ambient Intelligence and Humanized Computing. The final authenticated version is available online at: https://doi.org/10.1007/s12652-020-02233-2
Keywords: WDN, Leak, Flow, Pressure, Machine Learning
Subjects: Computing > Intelligent systems
Computing
Construction and engineering
Related URLs:
Depositing User: Ali Bahadori-Jahromi
Date Deposited: 12 Jun 2020 15:28
Last Modified: 28 Aug 2021 07:13
URI: https://repository.uwl.ac.uk/id/eprint/7038

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