Non-uniqueness in Contaminant Source Identification

The source of contamination in a water distribution system may be identified through a simulation-optimization approach. The optimization method searches for the contaminant source characteristics by iteratively estimating the contaminant plume concentrations until they match observations at sensors. The amount of information available for characterizing the source depends on the number and spatial locations of the sensors, as well as on the temporally varying stream of sensed data. The accuracy of the source characterization depends on the amount of observations available. A major factor affecting this accuracy is the degree of non-uniqueness present in the problem, which may cause misidentification of the source characteristics. As more sensors are added to the network, the non-uniqueness may be reduced and a unique solution may be identified. Thus, a key consideration when solving these problems is to assess whether the solution identified is unique, and if not, what other possible solutions are present. A systematic search for a set of alternatives that are maximally different in solution characteristics can be used to address and quantify non-uniqueness. For example, if the most different set of solutions that are identified by a search procedure are very similar, then that solution will be considered as the unique solution with a higher degree of certainty. Alternatively, identification of a set of maximally different solutions that vary widely in solution characteristics will indicate that non-uniqueness is present in the problem, and the range of solutions can be used as a general representation of the amount of non-uniqueness. This investigation uses an evolutionary algorithm (EA)-based alternatives generation procedures to quantify and address non-uniqueness present in a contaminant source identification problem for a water distribution network. As additional sensors may decrease the amount of non-uniqueness, different sensor configurations are being tested to investigate and quantify the improvement in uniqueness as more information is used in the source characterization.
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