Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 24
Articles | Volume 28, issue 24
https://doi.org/10.5194/hess-28-5443-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-5443-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 24
Research article
 | 
18 Dec 2024
Research article |  | 18 Dec 2024

Enhancing generalizability of data-driven urban flood models by incorporating contextual information

Tabea Cache, Milton Salvador Gomez, Tom Beucler, Jovan Blagojevic, João Paulo Leitao, and Nadav Peleg

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Short summary
We introduce a new deep-learning model that addresses the limitations of existing urban flood models in handling varied terrains and rainfall events. Our model subdivides a city into small patches and presents a novel approach to incorporate broader terrain information. It accurately predicts high-resolution flood maps across diverse rainfall events and cities (on minute and meter scales) that haven’t been seen by the model, which offers valuable insights for urban flood mitigation strategies.
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