Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 22, issue 4
Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1469-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-1469-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 4
Research article
 | 
26 Apr 2022
Research article |  | 26 Apr 2022

Machine-learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains

Andrea Magnini, Michele Lombardi, Simone Persiano, Antonio Tirri, Francesco Lo Conti, and Attilio Castellarin

Viewed

Total article views: 2,644 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,853 718 73 2,644 66 69
  • HTML: 1,853
  • PDF: 718
  • XML: 73
  • Total: 2,644
  • BibTeX: 66
  • EndNote: 69
Views and downloads (calculated since 15 Sep 2021)
Cumulative views and downloads (calculated since 15 Sep 2021)

Viewed (geographical distribution)

Total article views: 2,644 (including HTML, PDF, and XML) Thereof 2,505 with geography defined and 139 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
We retrieve descriptors of the terrain morphology from a digital elevation model of a 105 km2 study area and blend them through decision tree models to map flood susceptibility and expected water depth. We investigate this approach with particular attention to (a) the comparison with a selected single-descriptor approach, (b) the goodness of decision trees, and (c) the performance of these models when applied to data-scarce regions. We find promising pathways for future research.
Read more
Special issue
Advances in flood forecasting and early warning
Altmetrics
Final-revised paper
Preprint