Data Mining as a Tool to Identify Contaminant Sources in Water Distribution Systems

With growing concerns related to potential contamination ingress via backflow and/or terrorist threats to drinking water, useful methodologies are needed to assist in identifying locations from which ingress may have occurred. An efficient data mining approach in conjunction with a maximum likelihood procedure is described, which provides a means to identify the location and timing of an intrusion event, based on limited sensor data. The effectiveness of the data mining method is demonstrated using a case study network where it takes only approximately 5 minutes to identify an injection event using 5 sensors in a 285 node water distribution network. The effectiveness of the method is demonstrated using a number of alternative applications of the data mining methodology, which ensures the robustness of the methodology in locating potential sources of the ingress event.