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.
Graph Filters for Learning from Network Data, Graduate Level Course, Aalborg University, June 2021.
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 (email@example.com | +31(0)15-2784284 | www.dispuutwaterandenvironment.com).
- 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).
- Short-Term Water Demand Forecasting at District Level Using Deep Learning Methods (Diego Mauricio Corredor Mora).
- Applying deep learning vs machine learning models to reproduce dry spells at point scale from satellite information in a data-scarce region: the case of northern Ghana, 2021 (Panagiotis Mavritsakis).
- Exploration of Deep Learning-based Computer Vision for the Detection of Floating Plastic Debris in Waterways, 2021 (André J. Vallendar).
- Nowcasting heavy precipitation in the Netherlands: a deep learning approach, 2021 (Eva van der Kooij).
- Creation of new Extra-Tropical Cyclone fields in the North Atlantic with Generative Adversarial Networks, 2020 (Filippo Dainelli).
- Side-Channel Analysis with Graph Neural Networks, M. Sc. thesis, TU Delft, 2021 (V. de Bruijn).
- Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions, M. Sc. thesis, TU Delft, 2020 (M. Pocchiari).
- Identifying Author Fingerprints in Texts via Graph Neural Networks, M. Sc. thesis, TU Delft, 2020 (T. Sipko).
- Advances in Graph Signal Processing: Fast graph construction & Node-adaptive graph signal reconstruction, M. Sc. thesis, TU Delft, 2020 (M. Yang).
- Graph-Adaptive Activation Functions for Graph Neural Networks, M. Sc. thesis, TU Delft, 2020 (B. Iancu).
- Graph-Time Convolutional Neural Network: Learning from Time-Varying Signals Defined on Graphs, M. Sc. thesis, TU Delft, 2020 (G. Mazzola).