Automatic Anomaly Detection for Sewage Network Sensors

Alessandro Tibo, Thomas Dyhre Nielsen, Manfred Jaeger, Malthe Ahm, Peter Rasch

Research output: Contribution to conference without publisher/journalConference abstract for conferenceResearchpeer-review


The DONUT project (Ahm et. al, 2020) aims to provide a holistic view of the urban water system by making continuous water level measurements using a low-cost sensor network. A crucial instrument for ensuring the reliability of the network is the ability to detect anomalous events/ observations, e.g., due to faulty sensors or anomalous water conditions. In this paper, we propose two approaches (both based on machine learning models) to address this issue: an anomaly detection model for isolated sensors and a more expressive anomaly detection model relying on measurements from neighboring sensors. Our preliminary experiments show promising results for both approaches.
Original languageEnglish
Publication date2020
Publication statusPublished - 2020
EventIWA World Water Congress & Exhibition - Copenhagen , Denmark
Duration: 18 Oct 202023 Oct 2020


ConferenceIWA World Water Congress & Exhibition


  • Digitalization of water
  • Sensor networks
  • Deep Learning
  • Anomaly Detection


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