Optimal Water Quality Sensor Placement in Water Distribution Systems: A Computationally Cost-Effective Genetic Algorithm Framework

Despite advances in water treatment technologies and monitoring systems, contamination events in drinking water supply systems (DWSSs) still pose a threat to public health. Since timing is crucial in effectively mitigating impacts, the implementation of an early warning system (EWS) represents an optimal solution for securing the entire network. In this paper, we present a novel multi-objective approach based on the NSGA-II Genetic Algorithm (GA) for solving the sensor placement optimization (SPO) problem, aiming at defining the optimal water quality sensor system (WQSS) design. We start from the original formulation of the objective functions most commonly used in the literature, which aim, on the one hand, to reduce the impact and, on the other, to maximize the network coverage; such objective functions are rewritten in order to enable a comprehensive perspective of all potential contamination scenarios, including those that remain undetected by the WQSS. Furthermore, we address the issue of computational complexity, increasing with the size of the water distribution system (WDS), and we show that the proposed methodology is computationally cost-effective. Finally, we apply the methodology to two well-known benchmarking water distribution networks (WDNs), showcasing the capabilities and potential advantages it offers.