Publications
Conference Papers
2025
Exploring a Graph Regression Problem in River Networks
Under Review
Abstract: We investigate on a novel graph regression problem with interesting information bottlenecks and long range dependencies. We set various baselines using Graph Neural Network (GNN) models. In a detailed ablation study we show that GNNs have degenerated performance under noise compared to other architectures. A rarely mentioned limitation of GNNs.
Downloads:
2024
Spatio-Temporal Graph Neural Networks for Water Temperature Modeling
Published in Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition
Abstract: Recurrent Neural Networks do show their performance in time series modeling. So do Graph Neural Networks in modeling irregular neighboring structures. We propose a novel spatio-temporal architecture suited for water temperature modeling as well as other node-with-id networks.
Leveraging LSTM Embeddings for River Water Temperature Modeling
Published in IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition
Abstract: We propose a novel LSTM tailored for water temperature modeling using embeddings. Not only do we improve state-of-the-art performance by five percent, we also reduce the amount of learnable parameters by two orders of magnitude.
Impute Sensor Data in the Swiss River Network
Published in International Conference on Pattern Recognition Applications and Methods
Abstract: We use the connectivity of water stations to improve data imputing. We have a lot of missing data in our dataset and using the available measurements of neighboring stations improves the quality of data imputing by a lot.
Poster: View
2023
Graph Based Deep Learning on the Swiss River Network
Published in International Workshop on Graph-Based Representations in Pattern Recognition
Abstract: In this work we use the connectivity of water stations (namely the rivers). In a first step we predict the water temperature at each water station using an LSTM. Then we use the connectivity to refine these first predictions. Using the connectivity improves the state of the art at around 5%.
Theses
Imputing Gaps in Swiss River Dataset
Carlo Robbiani, 2024
Abstract: The process of measuring hydrological variables is not perfect. Due to scheduled service or malfunctions, data is missing. Depending on the case, a different strategy to fill these resulting gaps is required. This work uses the latest state-of-the-art models to fill each gap with the most suited method. The result is a gap free dataset with 80 stations over 40 years with an estimated RMSE of an astonishing 0.652.
Clustering of Hydrological Stations
Jan Zurbrügg, 2023
Abstract: In this work, a method has been developed to extract important characteristics of hydrological stations. These characteristics have been used to cluster stations with similarities in temperature and discharge patterns using an fully automated way.