Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs
This addresses security risks in public drinking water systems, though it appears incremental as it applies existing AI methods to a new domain-specific application.
The authors tackled the problem of monitoring drinking water reservoirs by developing an underwater acoustic sensor network with AI for event detection, classification, and localization, achieving results in both laboratory and real-world reservoir tests.
We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks. We implement an innovative AI-based approach to detect, classify and localize underwater events. In this paper, we describe the technology and cognitive AI architecture of the system based on one of the sensor networks, the hydrophone network. We discuss the challenges of installing and using the hydrophone network in a water reservoir where traffic, visitors, and variable water conditions create a complex, varying environment. Our AI solution uses an autoencoder for unsupervised learning of latent encodings for classification and anomaly detection, and time delay estimates for sound localization. Finally, we present the results of experiments carried out in a laboratory pool and the water reservoir and discuss the system's potential.