4Real: real-time urban pluvial flood forecasting

Urban pluvial floods, occurring when precipitation cannot be fully absorbed by the drainage system, cause flooding and substantial damages, as well as disruption to socio-economic activities. Their fast occurrence and relatively short duration mean that physically-based models for flood prediction are of limited use, due to their long computational runtime.
In this project, we will develop new Deep Learning (DL) methods to generate real-time flood predictions, such that they can be used to alert the population and to plan mitigation and rescue actions. Additionally, by exploiting hydraulic modelling knowledge and tightly integrating it with DL models, we aim to produce DL-based flood models which return spatially explicit, two-dimensional flood hazard maps with water depth, flood extent and flow velocity information. Tightly coupling the underlying hydraulic equations with a DL-framework will provide both, interpretability and adherence to physical constraints. The input data for the DL flood forecasting model will be rainfall forecasts provided by meteorological services (MeteoSwiss), images and 3D digital surface city models.
 

Project Partners:
Eawag, Swiss Federal Institute of Aquatic Science and Technology
SDSC, Swiss Data Science Center

Contacts:
Jan Dirk Wegner, ETH Zurich,
Stefania Russo, ETH Zurich,
Stefano D'Aronco, ETH Zurich,
Priyanka Chaudhary, ETH Zurich,
João Leitão, Eawag Zurich,