Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 11
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.
Special issue:
https://doi.org/10.5194/hess-28-2531-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 11
Research article
 | 
14 Jun 2024
Research article |  | 14 Jun 2024

Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

David F. Muñoz, Hamed Moftakhari, and Hamid Moradkhani

Viewed

Total article views: 1,578 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,191 331 56 1,578 81 46 45
  • HTML: 1,191
  • PDF: 331
  • XML: 56
  • Total: 1,578
  • Supplement: 81
  • BibTeX: 46
  • EndNote: 45
Views and downloads (calculated since 30 Jan 2024)
Cumulative views and downloads (calculated since 30 Jan 2024)

Viewed (geographical distribution)

Total article views: 1,578 (including HTML, PDF, and XML) Thereof 1,536 with geography defined and 42 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Nov 2024
Download
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.
Read more
Special issue
Methodological innovations for the analysis and management...