A Generalized Framework for Dynamic District Metered Area Sectorization in Water Supply Networks

Sectorization of Water Distribution Networks (WDNs) into District Metered Areas (DMAs) has become an essential strategy for improving leakage management, pressure regulation, water quality monitoring, and operational resilience. Existing sectorization methods, however, are often constrained by topology-based community detection, require the number of DMAs to be specified a priori, or inadequately represent the hydraulic characteristics and overlapping functional roles of network components. This study presents a generalized framework for dynamic DMA sectorization that integrates graph-theoretic concepts with hydraulic performance evaluation to produce compact, resilient, and operationally efficient network partitions. The framework introduces a fuzzy membership-based weighted modularity formulation that incorporates both the hydraulic significance of pipes through flexible edge-weighting schemes and the multiple community affiliations of boundary nodes using a degree-based membership propagation strategy. A greedy agglomerative optimization algorithm automatically identifies the optimal number of DMAs without requiring prior specification while minimizing inter-community connections. In the second stage, the framework determines the optimal placement of boundary valves and flow meters by maximizing network resilience while satisfying pressure and water quality constraints. The methodology is demonstrated on water distribution networks of varying size and complexity, illustrating its adaptability across different hydraulic and topological conditions. Results show that the proposed framework consistently produces fewer boundary pipes, preserves hydraulic performance, maintains acceptable water age, and offers greater operational flexibility than conventional unweighted modularity-based approaches. Owing to its modular structure, the framework can readily incorporate alternative hydraulic objectives, operational constraints, and real-time system information, making it a scalable foundation for intelligent and adaptive DMA planning in future smart water distribution systems.