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

Strategic Insights -

Research from the University of Udine in Italy has led to the development of a novel multi-objective approach based on the NSGA-II Genetic Algorithm (GA) for solving the sensor placement optimization (SPO) problem in water distribution systems. The approach aims to define the optimal water quality sensor system (WQSS) design, taking into account the reduction of contamination impacts and maximization of network coverage. The methodology is computationally cost-effective and has been applied to two well-known benchmarking water distribution networks (WDNs).

Key Takeaways:

  • The study proposes a novel multi-objective approach using the NSGA-II Genetic Algorithm (GA) for solving the sensor placement optimization (SPO) problem in water distribution systems.
  • The approach aims to define the optimal water quality sensor system (WQSS) design, balancing the reduction of contamination impacts and maximization of network coverage.
  • The methodology is computationally cost-effective, especially for large water distribution systems.
  • The study showcases the capabilities and potential advantages of the proposed approach by applying it to two well-known benchmarking water distribution networks (WDNs).
  • The research was conducted by a team of researchers from the University of Udine, led by Elia Zanelli.
  • Additional authors of the study include Matteo Nicolini and Daniele Goi.

Statistics:

  • The study proposes a novel approach based on the NSGA-II Genetic Algorithm (GA) for solving the SPO problem.
  • The approach aims to define the optimal WQSS design, balancing two objective functions: reducing contamination impacts and maximizing network coverage.
  • The methodology is computationally cost-effective, with a computational complexity that increases with the size of the water distribution system (WDS).
  • The study applied the methodology to two well-known benchmarking WDNs.
  • The research articles were published in the journal Water in 2025.

Sources:

  • Optimal Water Quality Sensor Placement In Water Distribution Systems: a Computationally Cost-effective Genetic Algorithm Framework. Water, 2025;17(18):2786.
  • University of Udine. (n.d.). Polytech Dept. Engn & Architecture. Via Sci 208, I-33100 Udine, Italy.
  • Mdpi. (n.d.). St Alban-Anlage 66, Ch-4052 Basel, Switzerland.