Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 16
Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4345-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4345-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 16
Review article
 | 
25 Aug 2022
Review article |  | 25 Aug 2022

Deep learning methods for flood mapping: a review of existing applications and future research directions

Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina

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Short summary
Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
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