Detecting and locating chemical intrusion in water distribution systems using 911 calls

Abstract

ABSTRACT Intentional or unintentional chemical contamination of water distribution systems (WDSs) could have severe health and socio-economic consequences. High potency chemicals constituting, in essence, “super poisons” have the potential to be used in such intrusion scenarios. Some of these contaminants are capable of killing the victim in less than 1 h. Due to their high toxicity levels and short time from exposure to onset of symptoms, 911 call centers are likely the first point of contact for victims or their families with the authorities. Information such as 911 calls could be used to identify the ongoing event and potential intrusion locations. In this way, such emergency calls could function as an intrusion warning system. This study employs network hydraulic modelling to synthesize the 911 call patterns in the aftermath of such events. It then defines the scenarios as a multi-label pattern recognition problem. The synthesized data then was used to train a Convolutional Neural Network (CNN). The trained AI was applied to a real-world WDS with approximately 4000 km of pipe and 26,000 demand nodes. The results indicated that CNN is capable of accurately recognizing the pattern and pinpointing the originating location of the intrusion with an accuracy greater than 93%.