AI research into resilient and sustainable water management

Urban water systems face growing pressure from climate change and demographics. As a result, water managers count increasingly on digitalization to ensure safe drinking water, adequate sanitation, and effective flood control.

Fast and accurate AI-tools are needed to model the physical processes within water networks and during flooding events. AidroLab believes that the most promising techniques to develop such tools are Graph Neural Networks or GNNs – an extension of deep learning to graph data structures. We are also exploring other AI-applications that can complement our GNN models and provide a thorough AI-based digital twin of (urban) water systems. By bringing together fundamental and applied AI, AidroLab is pushing the boundaries of science, enabling resilient and sustainable urban water systems.

AidroLab is part of the TU Delft AI Labs programme.

The team



Graph Filters for Learning from Network Data, Graduate Level Course, Aalborg University, June 2021.

Master Projects


MSc Thesis for CEG (Water Management and Environmental Engineering)

The upcoming thesis market for the WM and EE tracks of the Master in Civil Engineering will be hosted from 13th till the 20th of October 2021. AidroLab will be there proposing MSc Projects and Internships. Please check your BrightSpace page and the Google Sheet link of the Dispuut Water & Environment ( | +31(0)15-2784284 |

pdf-logo  MSc Thesis for EEMCS students.


  • Rainfall-Runoff Modelling at Basin Scale with a Global LSTM Neural Network (Katharina Wilbrand).
  • Using Deep Learning to Predict Flooding after a Dike Breach (Ron Bruijns).
  • Plastics Monitoring with Sonar (Samira I. Ibrahim).