Little knowledge isn't always dangerous - Understanding water distribution networks using centrality metrics

Abstract

Addressing nonrevenue water, a major issue for water utilities, requires identification of strategic metering locations using calibrated hydraulic models of the water network. However, calibrated hydraulic models use both static and dynamic network data and are often prohibitively expensive. We present an approach to understand water network operations that uses only the static information of the network. Specifically, we analyze water networks using augmented centrality measures. We use readily available static information about network elements (e.g., diameters of pipes) rather than calibrated dynamic information (e.g., roughness coefficients of pipes, demands at nodes), and model each network element appropriately for analysis using customized centrality measures. Our approach identifies: 1) pipes carrying higher flows; 2) nodes with higher delivery heads; and 3) pipes with higher failure impact. Each of the above helps in determining strategic instrumentation locations. We validate our analysis by comparison with fully calibrated hydraulic models for three benchmark topologies. Our experimental evaluation shows that centrality analysis yields results which have a match of more than 85% with those obtained using calibrated hydraulic models on benchmark networks without significant over-provisioning. We also present results from a real-life case study where our approach matched 78% with locations picked by experts.