Ensemble CNN Model for Effective Pipe Burst Detection in Water Distribution Systems

Sehyeong Kim, Sanghoon Jun, Donghwi Jung

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)

    Abstract

    Various data-driven anomaly detection methods have been developed for identifying pipe burst events in water distribution systems (WDSs); however, their detection effectiveness varies based on network characteristics (e.g., size and topology) and the magnitude or location of bursts. This study proposes an ensemble convolutional neural network (CNN) model that employs several burst detection tools with different detection mechanisms. The model converts the detection results produced by six different statistical process control (SPC) methods into a single compromise indicator and derives reliable final detection decisions using a CNN. A total of thirty-six binary detection results (i.e., detected or not) for a single event were transformed into a six-by-six grayscale heatmap by considering multiple parameter combinations for each SPC method. Three different heatmap configuration layouts were considered for identifying the best layout that provides higher CNN classification accuracy. The proposed ensemble CNN pipe burst detection approach was applied to a network in Austin, TX and improved the detection probability approximately 2% higher than that of the best SPC method. Results presented in this paper indicate that the proposed ensemble model is more effective than traditional detection tools for WDS burst detection. These results suggest that the ensemble model can be effectively applied to many detection problems with primary binary results in WDSs and pipe burst events.

    Original languageEnglish
    Pages (from-to)5049-5061
    Number of pages13
    JournalWater Resources Management
    Volume36
    Issue number13
    DOIs
    Publication statusPublished - 2022 Oct

    Bibliographical note

    Publisher Copyright:
    © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

    Keywords

    • Convolutional neural network
    • Ensemble
    • Pipe burst detection
    • Statistical process control methods
    • Water distribution system

    ASJC Scopus subject areas

    • Civil and Structural Engineering
    • Water Science and Technology

    Fingerprint

    Dive into the research topics of 'Ensemble CNN Model for Effective Pipe Burst Detection in Water Distribution Systems'. Together they form a unique fingerprint.

    Cite this