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    Real-Time Operational Response Methodology for Reducing Failure Impacts in Water Distribution Systems 

    Source: Journal of Water Resources Planning and Management:;2018:;Volume ( 144 ):;issue: 007
    Author(s): Mahmoud Herman A.;Kapelan Zoran;Savić Dragan
    Publisher: American Society of Civil Engineers
    Abstract: This paper presents a methodology for developing efficient and effective operational, short-time response to an unplanned failure event (e.g., a pipe burst or equipment failure) in a water distribution system (WDS). ...
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    Comparison of Multiobjective Optimization Methods Applied to Urban Drainage Adaptation Problems 

    Source: Journal of Water Resources Planning and Management:;2018:;Volume ( 144 ):;issue: 011
    Author(s): Wang Qi;Zhou Qianqian;Lei Xiaohui;Savić Dragan A.
    Publisher: American Society of Civil Engineers
    Abstract: This article compares three multiobjective evolutionary algorithms (MOEAs) with application to the urban drainage system (UDS) adaptation of a capital city in North China. Particularly, we consider the well-known NSGA-II, ...
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    Explicit Expressions for State Estimation Sensitivity Analysis in Water Systems 

    Source: Journal of Water Resources Planning and Management:;2018:;Volume ( 144 ):;issue: 004
    Author(s): Díaz Sarai;Mínguez Roberto;González Javier;Savic Dragan
    Publisher: American Society of Civil Engineers
    Abstract: The implementation of state estimation techniques to water systems enables the hydraulic state of a given network to be computed at any time. However, errors in both measurements and model parameters can severely affect ...
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    Usage of Multisource Data through Transfer Learning to Improve Short-Term Water Demand Forecasting 

    Source: Journal of Water Resources Planning and Management:;2025:;Volume ( 151 ):;issue: 010:;page 04025053-1
    Author(s): Li, Zilin; Liu, Guoxuan; Liu, Haixing; Savic, Dragan; Fu, Guangtao
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurate short-term water demand forecasting is essential for the operational management of water distribution systems. Previous research on the use of machine learning approaches to improve predictive accuracy has ...
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