A water distribution system is designed to deliver sufficient quantities of high quality water to consumers. The quality of water delivered in a WDS is difficult to maintain and monitor, as water quality can degrade within a water distribution system's pipes. A network of online monitoring stations designed to sample water quality in real-time, and to be strategically placed in a water distribution system has proven an effective method to monitor water quality and provide early warning of a contaminant intrusion A difficult task for an online monitoring station is to distinguish natural variability in water quality from variability caused by the presence of a contaminant in the water. Prior studies have shown how common water quality indicators (free chlorine, pH, conductivity, total organic carbon) respond to contaminants, and methods to ease the task of recognizing true contaminant presence. This work proposes an objective function incorporating the uncertainty in a monitoring station's detection employed in the framework of a genetic algorithm to place sensors at locations which minimize the expected consequence of a contamination event, and where water quality data are most indicative of true contamination events. Early warning systems composed of fixed water quality stations and inline mobile water quality sensors are designed in a multi-objective framework for a sample water distribution system.
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