Contaminant-Source Detection in a Water Distribution System Using the Ensemble Kalman Filter

Abstract

Early detection of a contamination leach into a water distribution system, followed by the identification of the source and an evaluation of the total amount of the contaminant that has been injected into the system, is of paramount importance in order to protect a water user’s health. The ensemble Kalman filter, which has been recently applied in hydrogeology to detect contaminant sources in aquifers, is extended to the identification of a contaminant source and its intensity in a water distribution system. The driving concept is the assimilation of contaminant observations at the nodes of the pipeline network at specified time intervals until enough information has been collected to allow the positioning of the source and the estimation of its intensity. Several scenarios are analyzed considering sources at different nodes, with different delays between the beginning of the pollution and the start of the measurements, different sampling time intervals, and different observation ending times. The scenarios are carried out in the benchmarking Anytown network, demonstrating the ability of the ensemble Kalman filter for contaminant-source detection in real water distribution systems. The use of the ensemble Kalman filter supposed a major breakthrough in the inverse modeling of subsurface flow and transport, and the successful results of its application to the synthetic Anytown network warrant further exploration of its capabilities in the realm of water distribution systems.