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We have built a novel system for the surveillance of drinking waterreservoirs using underwater sensor networks. We implement an innovativeAI-based approach to detect, classify and localize underwater events. In thispaper, we describe the technology and cognitive AI architecture of the systembased on one of the sensor networks, the hydrophone network. We discuss thechallenges of installing and using the hydrophone network in a water reservoirwhere traffic, visitors, and variable water conditions create a complex,varying environment. Our AI solution uses an autoencoder for unsupervisedlearning of latent encodings for classification and anomaly detection, and timedelay estimates for sound localization. Finally, we present the results ofexperiments carried out in a laboratory pool and the water reservoir anddiscuss the system's potential.

Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs