Scaled Multiobjective Optimization of an Intensive Early Warning System for Water Distribution System Security

Abstract

Performance of an early warning system composed of online monitoring sensors for protecting municipal water supply is dependent on the number of sensors deployed. The inherent trade-off of performance versus scale of the system implemented is explored in this paper through multiobjective optimization using an augmented messy genetic algorithm (mGA). The augmented messy GA facilitated the comparison of solutions with variability in the number of sensors deployed. In this paper an early warning system is represented by a system of fixed sensors placed at network junctions, inline mobile sensors deployed from network junctions carried by flow within network pipes, and surface transceivers to communicate wirelessly with mobile sensors for data transmission and analysis. Performance of the implemented early warning system was measured as the time required for contamination detection, the detection likelihood, the population affected prior to event detection, and the total system cost for a small-, medium-, and large-scale municipal network. Results show well-defined Pareto fronts for each objective versus the cost of each solution, providing a tool for designers to optimize budget decisions.