Pressure sensor placement for water distribution network calibration based on graph-theoretic approaches

An accurately calibrated hydraulic model of a water distribution network (WDN) is crucial for tasks such as leakage detection, resilience assessment, and rehabilitation planning. Since model calibration relies on real data, increasing monitoring locations for pressure and flow raises both accuracy and cost. Therefore, selecting suitable data collection points is crucial for efficient calibration. This research proposes a practical, graph-theory-inspired approach for determining pressure sensor locations based on the network topology and the demand distribution in the WDN. The method employs a customized metric of Node Demand Betweenness Centrality (NDBC) as a criterion to estimate flow distribution, and thereby identifies influential paths within WDNs. Sensor locations are then optimized using the nondominated sorting genetic algorithm II (NSGA-II), which identifies nodes located along the most influential paths in WDNs while maximizing network coverage by increasing the graph-based hydraulic distance between sensors. This methodology is further supported by additional graph analysis methods, which serve as a faster alternative and are compared against a widely utilized hydraulic model-based approach. The proposed approach was applied to both the Modena and L-Town WDNs. In both case studies, the calibrated models based on pressure data from the sensor locations proposed in this research demonstrated satisfactory accuracy.