Intentional chemical contamination of water distribution systems (WDSs) could have severe health 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 hour. Due to their high toxicity levels and short period of time from exposure to the onset of symptoms, 9-1-1 call centers are likely the first point of contact for the victims or their families with the authorities. Information such as 9-1-1 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 modeling to synthesize the 9-1-1 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 artificial intelligence (AI), was applied to a real-world WDS with approximately 4,000 km of pipe and 26,000 demand nodes. The results indicated that the CNN is capable of accurately recognizing the pattern and pinpointing the originating location of the intrusion with an accuracy greater than 93%.

  • Intentional chemical intrusion in water distribution systems (WDSs) is a significant system vulnerability.

  • No sensory system is currently available that can detect synthetic opioids in (WDS) online.

  • 9-1-1 call logs were used to define the detection of chemical intrusion as a pattern recognition problem.

  • Deep learning AI, trained by water quality simulation outcomes, can detect these events and locate the point of intrusion.

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