Bochum students explore deep learning techniques for rainfall runoff predictions
In recent years, the applicability of Machine Learning approaches for hydrological use cases has reached great attendance. Especially long short-term memory (LSTM) networks show comparable results to conceptual models when being applied to hydrological time series forecasting problems. Now, Interdisciplinary students from Bochum explored innovative deep learning techniques for rainfall-runoff modeling.
Efficient Earth Observation data processing relying on CODE-DE
In April 2020, a relaunch of the Copernicus Data and Exploitation Platform – Deutschland (CODE-DE) was announced. This was a big deal, since the second generation of the CODE-DE platform should now offer public access to the long awaited online processing environment. At this time, our WaCoDiS research project was in its final term and we were ready to validate our developments as part of a pre-operational deployment. Thus, the relaunch of CODE-DE just came at the right time.
This post will also be EGU Session ESSI3.1 Display D909 at EGU2020: Sharing Geoscience Online. We will participate in the chat on Monday, May 4th from 16:15 to 18:00 to discuss our approach and findings.
Automated Workflows for Creating EO Products
Earth Observation data has become available and obtainable in continuously increasing quality as well as spatial and temporal coverage. To deal with the massive amounts of data, the WaCoDiS project aims to develop an architecture that allows its automated processing. The project focuses on the development of innovative water management analytics services based on Earth Observation data such as provided by the Copernicus Sentinel missions.