Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 8
Articles | Volume 25, issue 8
https://doi.org/10.5194/nhess-25-2845-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2845-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 8
Research article
 | 
25 Aug 2025
Research article |  | 25 Aug 2025

BN-FLEMOΔ: a Bayesian-network-based flood loss estimation model for adaptation planning in Ho Chi Minh City, Vietnam

Kasra Rafiezadeh Shahi, Nivedita Sairam, Lukas Schoppa, Le Thanh Sang, Do Ly Hoai Tan, and Heidi Kreibich

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,051 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,029 17 5 1,051 18 8
  • HTML: 1,029
  • PDF: 17
  • XML: 5
  • Total: 1,051
  • BibTeX: 18
  • EndNote: 8
Views and downloads (calculated since 02 Jan 2025)
Cumulative views and downloads (calculated since 02 Jan 2025)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,051 (including HTML, PDF, and XML) Thereof 974 with geography defined and 77 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Sep 2025
Download
Short summary
Ho Chi Minh City (HCMC) faces severe flood risks from climatic and socio-economic changes, requiring effective adaptation solutions. Flood loss estimation is crucial, but advanced probabilistic models accounting for key drivers and uncertainty are lacking. This study presents a probabilistic flood loss model with a feature selection paradigm for HCMC’s residential sector. Experiments using new survey data from flood-affected households demonstrate the model's superior performance.
Read more
Share
Altmetrics
Final-revised paper
Preprint