Publikationen

Image-based methods for real-time water level estimation

Xabier Blanch, Anette Eltner, Ralf Hedel, Jens Grundmann

Application of optical Particle Tracking Velocimetry (PTV) to determine continuous discharge time series

André Kutscher, Jens Grundmann, Anette Eltner, Xabier Blanch, Ralf Hedel

Flood Forecasting with Deep Learning LSTM Networks: Local vs. Regional Network Training Based on Hourly Data

Tanja Morgenstern, Jens Grundmann, Niels Schütze

Improving flood forecasting for small catchments using outlier and anomaly detection combined with deep learning networks

Betreuer: Tanja Morgenstern, Niels Schütze

Mugabo Gitare und Clarisse Umugwaneza (Gruppenarbeit)

Flood forecasting for small catchments in Saxony (Germany) using Long Short-Term Memory Networks

Betreuer: Tanja Morgenstern, Niels Schütze

Evaluation of physics-based deep learning model MC-LSTM for short-term forecasting

Betreuer: Nicola Balbarini, Laura Frølich, Niels Schütze

Determination of continuous discharge time series based on the optical Particle Tracking Velocity (PTV)

André Kutscher, Jens Grundmann, Anette Eltner, Xabier Blanch, Ralf Hedel

Towards automatic real-time water level estimation using surveillance cameras

Xabier Blanch, Anette Eltner, Ralf Hedel, Jens Grundmann

Towards automatic real-time water level estimation using surveillance cameras

Flood Forecasting With LSTM Networks: Enhancing the Input Data With Statistical Precipitation Information

Tanja Morgenstern, Jens Grundmann, Niels Schütze

Flood Forecasting With LSTM Networks: Enhancing the Input Data With Statistical Precipitation Information

Wasserstandsmessungen mit Deep Learning

Image-based gauging stations can allow for significant densification of monitoring networks of river water stages. However, thus far, …

Wasserstandsmessungen mit Deep Learning

Hochwasservorhersage mit LSTM

Tanja Morgenstern, Niels Schütze

Hochwasservorhersage mit LSTM