Backward Probabilistic Modeling to Identify Contaminant Sources in Water Distribution Systems

Abstract

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 it travels through the system. To minimize the adverse impact of the contaminant release, the source must be characterized to determine the extent of the contamination and to remediate the contaminated area. We present a backward modeling approach that uses the data collected by the sensors to obtain probability density functions that describe the random time in the past that the observed contamination was at a particular upgradient position. These probability density functions can be used to identify the source node and release time. The approach is developed for steady flow conditions with known system demands and for a single, instantaneous source of contamination. Using a hypothetical water distribution system and release scenario, we demonstrate that the backward model is an efficient and effective approach for identifying the source node and the release time.