Conditioned Backward Probability Modeling to Identify Contamination Sources in a Water Distribution System

摘要

If a chemical or biological agent is released into a water distribution system,sensors that are installed in the pipe network may detect the contamination as ittravels through the system. To minimize the adverse impact of the contaminantrelease, the source must be characterized to determine the extent of the contaminationand to remediate the contaminated area. We develop an adjoint-based backwardmodeling approach that uses the collected sensor data to identify the location andtiming of the release. In the adjoint model, the sensor location is treated as a sourceof probability, and the probability is transported upgradient and backward in time toobtain probability density functions (PDFs) that describe the random time in the pastthat the observed contamination was at a particular upgradient position. These PDFscan be used to identify the source location and release time. Conditioning these PDFson the measured concentrations improves their accuracy and decreases theiruncertainty. We demonstrate the effectiveness of this method by using it tocharacterize a contaminant source in a hypothetical water distribution system.