Machine Learning for Water Resource Management

As part of this research project, possibilities for data-driven support and optimization of the control and planning processes of water and wastewater management systems are to be determined, examined and implemented using machine learning methods.
One research focus is the generation of statistically oriented forecasts of the temporal evolution of operating and planning-relevant key figures.
Furthermore, it should be investigated how information that is relevant and interpretable for the operator can be extracted from the generated statistical variables and made available in order to generate an operation-oriented added value for operators with different knowledge horizons in the statistical data analysis and thus to ensure a successful integration of the developed methods .


  • PhD student: Björn Sonnenschein
  • Supervisor: Prof. Dr. Mark Oelmann (HRW, Wirtschaftsinstitut)
  • Supervisor: Prof. Dr. Florian Ziel (UDE, Umweltökonomik)
  • Mentor: Vertreter:in der Stadtentwässerungsbetriebe Köln