A K-Sensor correlation-based evolutionary optimization algorithm to cluster contamination events and place sensors in water distribution systems

Contaminants that are introduced to drinking water systems can threaten large populations, and the potential for catastrophic consequences accentuates the need for efficient post-disaster strategies, including optimal hydrant flushing. Efficient hydrant flushing can significantly reduce impacts on public health, but performance relies on information about the propagation of a contaminant and the affected regions in a water network. While observations from water quality sensors are useful in timely detections of contaminants, little information on its source, propagation, and affected regions can be inferred. In the absence of such information, opening or closing hydrants might not help discharge contaminants but could accelerate propagation of a plume through the water network due to drops in pressure. To address this limitation of sensor layout optimization models, this research has developed a new model to identify the optimal location of sensors to effectively support hydrant flushing mechanisms. The model has been developed in three steps: (1) contamination events were simulated in a water network; (2) spatially similar propagating contamination events were identified; and (3) the layout of water quality sensors was optimized. In the first step, a representative number of potential contamination events were simulated using a hydraulic model. The second step clustered contamination events based on spatial similarity in their propagation regimes. Finally, the last step identified locations for placing water quality sensors within clusters (identified in the previous step) while minimizing detection time and maximizing probability of detection. This model ensures that when a sensor alarm is activated, contaminated region where hydrants should be opened or closed are spatially restricted. The approach developed in this research was applied to design a sensor network for a benchmark case study, Mesopolis. The layout of 10 water quality sensors was optimized over a set of 9161 contamination events, leading to 76% probability of detection with an average detection time of 8.2 h. The solution was compared with sensor layouts based on existing approaches, and it was found that the new approach could improve the mass of contaminant that was removed from the pipe network through hydrant flushing strategies. The new approach model improves the effectiveness of hydrant flushing strategies by restricting the area where hydrants are flushed to predefined zones based