Coupling of logistic regression analysis and local search methods for characterization of water distribution system contaminant source

Accidental or intentional drinking water contamination has long been and remains a major threat to water security throughout the world. An inverse problem can be constructed, given sensor measurements in a water distribution system (WDS), to identify the contaminant source characteristics by integrating a WDS simulation model with an optimization method. However, this approach requires numerous compute-intensive simulation runs to evaluate potential solutions; thus, determining the best source characteristic within a reasonable computational time is challenging. In this paper, we describe the development of a WDS contamination characterization algorithm by coupling a statistical model with a heuristic search method. The statistical model is used to identify potential source locations of contamination and a local search aims at further refining contaminant source characteristics. Application of the proposed approach to two illustrative example water distribution networks demonstrates its capability of adaptively discovering contaminant source characteristics as well as evaluating the degree of non-uniqueness of solutions. The results also showed that the local search as an optimizer has better performance than a standard evolutionary algorithm (EA).