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    Near Real-Time Burst Detection Method Based on Multistep Forecasting Scheme and Local Residual Discrepancy

    Source: Journal of Water Resources Planning and Management:;2025:;Volume ( 151 ):;issue: 008::page 04025033-1
    Author:
    Wan, X.
    ,
    Farmani, R.
    ,
    Keedwell, E.
    ,
    Zhou, X.
    DOI: 10.1061/JWRMD5.WRENG-6850
    Publisher: American Society of Civil Engineers
    Abstract: AbstractOnline detection of burst events based on flow time series data is an efficient and cost-effective way to monitor the water distribution system. However, the effectiveness of anomaly detection algorithms can vary depending on the dataset, as the ...
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      Near Real-Time Burst Detection Method Based on Multistep Forecasting Scheme and Local Residual Discrepancy

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313932
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    • Journal of Water Resources Planning and Management

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    contributor authorWan, X.
    contributor authorFarmani, R.
    contributor authorKeedwell, E.
    contributor authorZhou, X.
    date accessioned2026-08-20T21:05:22Z
    date available2026-08-20T21:05:22Z
    date copyright2025/06/12
    date issued2025
    identifier otherJWRMD5.WRENG-6850.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313932
    description abstractAbstractOnline detection of burst events based on flow time series data is an efficient and cost-effective way to monitor the water distribution system. However, the effectiveness of anomaly detection algorithms can vary depending on the dataset, as the ...
    publisherAmerican Society of Civil Engineers
    titleNear Real-Time Burst Detection Method Based on Multistep Forecasting Scheme and Local Residual Discrepancy
    typeJournal Article
    journal volume151
    journal issue8
    journal titleJournal of Water Resources Planning and Management
    identifier doi10.1061/JWRMD5.WRENG-6850
    journal fristpage04025033-1
    journal lastpage04025033-13
    page13
    treeJournal of Water Resources Planning and Management:;2025:;Volume ( 151 ):;issue: 008
    contenttypeFulltext
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