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<p>The efficient management of water distribution networks (WDNs) is crucial for ensuring sustainable urban water supply. In this paper, we present a novel framework that utilizes the Distance Laplacian Matrix (DLM) to analyze and optimize WDNs. The DLM integrates distance information into the Laplacian matrix, offering valuable insights into network connectivity and dynamics. We introduce a comprehensive methodology that employs spectral analysis techniques based on the DLM to tackle key challenges in WDN management, such as critical node identification, network resilience assessment, and structural optimization. Through case studies and practical implementations, we illustrate the effectiveness of our approach in improving decision-making processes and enhancing the efficiency and resilience of WDNs. The framework offers a novel suite of Distance Laplacian Matrix techniques tailored to enhance core water management tasks and streamline the identification of water losses through optimal network partitioning. This study proposes a novel framework using the Distance Laplacian Matrix (DLM) for efficient water distribution network management. The DLM incorporates distance metrics into spectral analysis, which provides enhanced network connectivity and performance insights. Through the application of DLM-based spectral clustering, critical nodes, resilience assessments, and network optimization tasks are handled effectively. Case studies demonstrate the applicability of the framework in urban water networks, ensuring improved decision making for network partitioning and resource distribution.</p>

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An Efficient Supply Management in Water Flow Network Using Graph Spectral Techniques Based on a Distance Laplacian Matrix

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Tamilselvi Gopalsamy,
  • Vasanthi Thankappan,
  • Sundar Chandramohan

摘要

The efficient management of water distribution networks (WDNs) is crucial for ensuring sustainable urban water supply. In this paper, we present a novel framework that utilizes the Distance Laplacian Matrix (DLM) to analyze and optimize WDNs. The DLM integrates distance information into the Laplacian matrix, offering valuable insights into network connectivity and dynamics. We introduce a comprehensive methodology that employs spectral analysis techniques based on the DLM to tackle key challenges in WDN management, such as critical node identification, network resilience assessment, and structural optimization. Through case studies and practical implementations, we illustrate the effectiveness of our approach in improving decision-making processes and enhancing the efficiency and resilience of WDNs. The framework offers a novel suite of Distance Laplacian Matrix techniques tailored to enhance core water management tasks and streamline the identification of water losses through optimal network partitioning. This study proposes a novel framework using the Distance Laplacian Matrix (DLM) for efficient water distribution network management. The DLM incorporates distance metrics into spectral analysis, which provides enhanced network connectivity and performance insights. Through the application of DLM-based spectral clustering, critical nodes, resilience assessments, and network optimization tasks are handled effectively. Case studies demonstrate the applicability of the framework in urban water networks, ensuring improved decision making for network partitioning and resource distribution.