TANTao,ZHANG Miao,JIANGYunlong,et al.Topological Distance?based Clustering Method for Water Supply Network Partitioning[J].China Water & Wastewater,2026,42(4):10-16.
基于拓扑距离聚类的供水管网分区方法
- Title:
- Topological Distance?based Clustering Method for Water Supply Network Partitioning
- 关键词:
- 供水管网; 减压阀; 泵站; 拓扑距离; K-means聚类法
- Keywords:
- water supply network; pressure reducing valve; pumping station; topological distance; K-means clustering method
- 摘要:
- 对供水管网进行分区计量简化压力管理，可有效降低漏损率。然而，当前相关研究往往局限于节点拓扑关系，忽略了减压阀和泵站对分区结果的影响。针对该问题，提出了一种基于拓扑距离聚类的供水管网分区方法，该方法将减压阀和泵站作为分区边界的依据，进而提高分区的准确性。首先，将管网映射成为无向图用于构建邻接矩阵，并放大减压阀和泵站节点间的拓扑距离。然后，采用弗洛伊德最短路径算法构建拓扑距离矩阵。最后，基于构建的拓扑距离矩阵采用K-means聚类法对节点聚类，进而实现管网分区。将所提方法应用于3个管网案例并与两种传统方法对比，结果表明，基于拓扑距离聚类的分区法能够得到更准确的分区结果，同时保证分区具有较高的模块度与均匀性。
- Abstract:
- District metering is a method of simplifying pressure management by partitioning the water supply network, which can effectively reduce leakage rates. However, current research often focuses solely on the topological relationship of nodes, ignoring the influence of pressure reducing valves and pumping stations on partitioning outcomes. To address this issue, a topological distance-based clustering method for water supply network partitioning is proposed. This method uses pressure reducing valves and pumping stations as the basis for partition boundaries, thereby enhancing partitioning accuracy. Initially, the water supply network is modeled as an undirected graph to construct the adjacency matrix, and the topological distances between pressure reducing valves and pumping stations are magnified. Subsequently, the topological distance matrix is generated using Floyd’s shortest path algorithm. Finally, nodes are clustered based on the constructed topological distance matrix using the K-means clustering method, thus facilitating the partitioning of the water supply network. The proposed method was applied to three case studies and compared with two traditional methods. The results indicate that the proposed partitioning algorithm based on topological distance clustering, yields more accurate partitioning outcomes while ensuring high modularity and uniformity in the partitioning process.
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