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Join us for a 2-day Workshop on “Data-driven hydrology and machine learning algorithms for water management and risk assessment” organized by the STARS4Water and DRHYM (data-driven hybrid hydrological models) projects.
The hydrological community has been paying particular attention to the advantages of using machine-learned hydrologic models or model components in the last years. Their aims span over a variety of open opportunities for research and operations in the water sector. From input data processing to model output prediction, novel techniques have been explored to support a large number of hydrological applications, such as: forecasting of extreme events (floods and droughts), predicting water use patterns and projecting future water demand, detecting patterns in reservoir management and predicting releases, regionalizing hydrological model’s parameters over large river basins, regionalizing groundwater information at the catchment scale.
This workshop aims to bring together hydrologists and data-driven experts to explore pathways to bring new data sets and data-driven modelling approaches from hydrologic research to the more practical and operational planning level in river basins. By participating in this workshop, you will: (1) gain an understanding of differentiable models, (2) develop hands-on experience building and applying deep learning models for rainfall-runoff modeling, and (3) get a feeling for the role of architectures and data. Most importantly, however, the workshop will provide you with a good foundation for continued exploration of deep learning in environmental applications.
When: Wednesday 24 April 13:30 CEST to Thursday 25 April 13:30 CEST
Where: INRAE, 1 rue Pierre-Gilles de Gennes, 92160 Antony, France
Participants: STARS4Water & DRHYM partners and registered attendees
Format: On-site & Online (Session I and Session II) & On-site only (Session III)
PROGRAMME
Here you will find further details about the Programme
REGISTRATION
Secure your spot for this event, by registering here.
COMPASS aims to develop a harmonised, yet flexible, methodological framework for climate and impact attribution of various complex extremes that includes compound, sequences and cascading hazard events
The COMPASS project has received funding from the European Union’s HORIZON Research and Innovation Actions Programme under Grant Agreement No. 101135481
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or of the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them.