Evolutionary Computation-Based Methods for Characterizing Contaminant Sources in a Water Distribution System

Abstract

The area of systematic identification of contamination sources in water distribution systems is in its infancy and is rapidly growing. The real water distribution network problem poses many challenges that current methods usually assume away to facilitate manageable method development and testing. Current methods may not readily and efficiently address issues, such as multiple sources, unknown contamination types with different reaction kinetics, use of different types of sensors with varying degree of resolution, dynamically varying demand and sensor information, and uncertainty and errors in the data and measurements. With the aim of addressing these imminent challenges, this paper reports the findings of an ongoing research investigation that develops and tests an evolutionary algorithm-based flexible and generic procedure, which is structured within a simulation-optimization paradigm. This paper describes the specific implementation of the method using evolution strategies (ESs), a population-based heuristic global search algorithm. A key component of designing this source characterization method is to define a compact, but comprehensive, solution encoding structure. The new method is constructed using a tree-based encoding design to enable the representation of variable-length decision vectors and a set of associated genetic operators that enable an efficient search. This algorithm is successfully tested and demonstrated to have consistently good performance for several instances of an illustrative water distribution contamination case study. As the ES-based algorithm conducts a probabilistic search, its robustness is tested using multiple random trials, and the method is shown to exhibit a robust behavior.