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A platform for research: civil engineering, architecture and urbanism
Topology-distance-based clustering method for water distribution network partitioning
Partitioning water distribution networks (WDNs) into district metered areas offers benefits including reduced nonrevenue water and simplified pressure management. However, current research in this field tends to narrowly focus on the topological relationships among nodes, often overlooking the influence of pressure reducing valves (PRVs) and pump stations on clustering results. To address this limitation, this study introduces a topology-distance-based clustering (TDBC) method that enhances the accuracy of partitioning by explicitly considering the impact of PRVs and pump stations. In the TDBC method, the Floyd algorithm is initially employed to construct a topological distance matrix that quantifies the degree of node connectivity. By amplifying topological distances for links including PRVs and pump stations, their effect on clustering results is incorporated. Subsequently, nodes are clustered using the K-means algorithm based on the resulting topology-distance matrix. The proposed TDBC approach is applied to four network cases, and its outcomes are compared with those of two traditional methods. The comparative analysis indicates that the TDBC algorithm achieves precise partitioning results for networks incorporating PRVs or pump stations, while ensuring a harmonious balance between modularity and the uniformity of the partitioning results, even in networks with complex structures and highly interconnected loops. HIGHLIGHTS Incorporation of PRVs and pump stations: The TDBC method introduces a novel approach by explicitly incorporating the influence of pressure reducing valves (PRVs) and pump stations into the clustering process.; Topological distance-based clustering: The TDBC method shifts from traditional geographical proximity-based methods to a topological distance-based approach.; Improved clustering uniformity.;
Topology-distance-based clustering method for water distribution network partitioning
Partitioning water distribution networks (WDNs) into district metered areas offers benefits including reduced nonrevenue water and simplified pressure management. However, current research in this field tends to narrowly focus on the topological relationships among nodes, often overlooking the influence of pressure reducing valves (PRVs) and pump stations on clustering results. To address this limitation, this study introduces a topology-distance-based clustering (TDBC) method that enhances the accuracy of partitioning by explicitly considering the impact of PRVs and pump stations. In the TDBC method, the Floyd algorithm is initially employed to construct a topological distance matrix that quantifies the degree of node connectivity. By amplifying topological distances for links including PRVs and pump stations, their effect on clustering results is incorporated. Subsequently, nodes are clustered using the K-means algorithm based on the resulting topology-distance matrix. The proposed TDBC approach is applied to four network cases, and its outcomes are compared with those of two traditional methods. The comparative analysis indicates that the TDBC algorithm achieves precise partitioning results for networks incorporating PRVs or pump stations, while ensuring a harmonious balance between modularity and the uniformity of the partitioning results, even in networks with complex structures and highly interconnected loops. HIGHLIGHTS Incorporation of PRVs and pump stations: The TDBC method introduces a novel approach by explicitly incorporating the influence of pressure reducing valves (PRVs) and pump stations into the clustering process.; Topological distance-based clustering: The TDBC method shifts from traditional geographical proximity-based methods to a topological distance-based approach.; Improved clustering uniformity.;
Topology-distance-based clustering method for water distribution network partitioning
Kun Du (author) / Jiangyun Li (author) / Wei Xu (author) / Zilian Liu (author) / Feifei Zheng (author)
2023
Article (Journal)
Electronic Resource
Unknown
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