Optimal Sampling Locations to Reduce Uncertainty in Contamination Extent in Water Distribution Systems

Abstract

Drinking water utilities rely on samples collected from the distribution system to provide assurance of water quality. If a water contamination incident is suspected, samples can be used to determine the source and extent of contamination. By determining the extent of contamination, the percentage of the population exposed to contamination, or areas of the system unaffected can be identified. Using water distribution system models for this purpose poses a challenge because significant uncertainty exists in the contamination scenarios (e.g., injection location, amount, duration, customer demands, and contaminant characteristics). This article outlines an optimization framework to identify strategic sampling locations in water distribution systems. The framework seeks to identify the best sampling locations to quickly determine the extent of the contamination while considering uncertainty with respect to the contamination scenarios. The optimization formulations presented here solve for multiple optimal sampling locations simultaneously and efficiently, even for large systems with a large uncertainty space. These features are demonstrated in two case studies.