Machine learning-based anomaly detection for real-time monitoring of urban waste water networks

Lennart Schmidt; Felix Weise; Manfred Schütze; Phillip Grimm; Julius Polz; Jan Bumberger

doi:https://doi.org/10.5194/egusphere-egu23-15851

[Back] [Session ITS1.10/NP0.1]

EGU23-15851

https://doi.org/10.5194/egusphere-egu23-15851

EGU General Assembly 2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine learning-based anomaly detection for real-time monitoring of urban waste water networks

Lennart Schmidt

¹, Felix Weise², Manfred Schütze

³, Phillip Grimm², Julius Polz

⁴, and Jan Bumberger¹

Lennart Schmidt et al.

¹Monitoring and Exploration Technologies, Helmholtz-Centre for Environmental Research (UFZ), Leipzig, Germany
²Grimm Water Solutions UG, Freiburg, Germany
³Institute for Automation und Communication (ifak), Magdeburg, Germany
⁴Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (Campus Alpin), Garmisch-Partenkirchen, Germany

How to cite: Schmidt, L., Weise, F., Schütze, M., Grimm, P., Polz, J., and Bumberger, J.: Machine learning-based anomaly detection for real-time monitoring of urban waste water networks, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15851, https://doi.org/10.5194/egusphere-egu23-15851, 2023.

This abstract has been withdrawn on 19 Apr 2023.