Considering Demand Variability and Measurement Uncertainties in Adaptive Source Characterization in Water Distribution Networks

Characterizing the sources of contamination in water distribution networks continues to be a challenging problem. Several methods have been reported to address this problem. The authors previously presented and continue to investigate an adaptive search procedure that attempts to solve this problem under dynamic conditions. Since demand variability and measurement errors contribute significantly to the quality of the solutions obtained as well as the time to solve the problem, we investigate these effects on the adaptive dynamic optimization procedure. First, the effects of these variabilities and uncertainties on the solutions obtained under deterministic conditions are evaluated. Second, we incorporate them such that the search for the source characterization is conducted under noisy conditions. This paper reports the results from these investigations based on an investigation conducted for an illustrative water distribution network.