Articles | Volume 24, issue 10
https://doi.org/10.5194/nhess-24-3537-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-24-3537-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Oct 2024
Research article |
| 17 Oct 2024
| 17 Oct 2024
Transferability of machine-learning-based modeling frameworks across flood events for hindcasting maximum river water depths in coastal watersheds
Maryam Pakdehi
Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Ebrahim Ahmadisharaf
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Behzad Nazari
Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76010, USA
Eunsaem Cho
Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
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Short summary
Machine learning (ML) algorithms have increasingly received attention for modeling flood events. However, there are concerns about the transferability of these models (their capability in predicting out-of-sample and unseen events). Here, we show that ML models can be transferable for hindcasting maximum river flood depths across extreme events (four hurricanes) in a large coastal watershed (HUC6) when informed by the spatial distribution of pertinent features and underlying physical processes.
Machine learning (ML) algorithms have increasingly received attention for modeling flood events....
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