Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2531-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2531-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jun 2024
Research article |
| 14 Jun 2024
| 14 Jun 2024
Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models
David F. Muñoz
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24060, USA
Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL 35487, USA
Hamed Moftakhari
Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL 35487, USA
Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Hamid Moradkhani
Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL 35487, USA
Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
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Short summary
Linking hydrodynamics with machine learning models for compound flood modeling enables a robust characterization of nonlinear interactions among the sources of uncertainty. Such an approach enables the quantification of cascading uncertainty and relative contributions to total uncertainty while also tracking their evolution during compound flooding. The proposed approach is a feasible alternative to conventional statistical approaches designed for uncertainty analyses.
Linking hydrodynamics with machine learning models for compound flood modeling enables a robust...
