Data-efficient and risk-based pressure sensor placement optimization for water systems

Abstract

Deployment of remote metering infrastructure can help utilities improve pressure management and limit pipe break impacts. Since it is cost-infeasible to install sensors to fully cover an entire system, a risk-based approach where devices are deployed in areas of systemic vulnerabilities will maximize the benefit of these limited resources. Previous work on sensor allocations requires well-calibrated hydraulic models that contain a full asset inventory. This high barrier of data requirement makes it difficult to apply existing methods. In this research, we present a more efficient methodology with lower data requirements. Proxies for system vulnerabilities are generated using historic break records, and GIS inventories are used to derive candidate installation points. The problem is presented as a generalized version of the well-studied maximal coverage location problem, where locations are selected for optimal coverage of risky areas. The workflow is demonstrated on a small suburban utility in Pennsylvania, USA.