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

2021/07/29 by Jörg Stork, Stork, Jörg, Philip Wenzel +19
Earth and Planetary Sciences · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Underwater Acoustics Research #Underwater Vehicles and Communication Systems #Water Systems and Optimization

paper · pdf · doi:10.48550/arxiv.2107.13977

openalex publication_date 2021/07/29 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

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.

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