Section Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany
Planetary Boundaries Science Lab, Earth System Analysis, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Section Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany
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: 4,497 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
4,162
265
70
4,497
78
69
HTML: 4,162
PDF: 265
XML: 70
Total: 4,497
BibTeX: 78
EndNote: 69
Views and downloads (calculated since 02 Jan 2025)
Cumulative views and downloads
(calculated since 02 Jan 2025)
Total article views: 3,054 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,727
265
62
3,054
78
69
HTML: 2,727
PDF: 265
XML: 62
Total: 3,054
BibTeX: 78
EndNote: 69
Views and downloads (calculated since 25 Aug 2025)
Cumulative views and downloads
(calculated since 25 Aug 2025)
Total article views: 1,443 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,435
0
8
1,443
0
0
HTML: 1,435
PDF: 0
XML: 8
Total: 1,443
BibTeX: 0
EndNote: 0
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: 4,497 (including HTML, PDF, and XML)
Thereof 4,361 with geography defined
and 136 with unknown origin.
Total article views: 3,054 (including HTML, PDF, and XML)
Thereof 2,918 with geography defined
and 136 with unknown origin.
Total article views: 1,443 (including HTML, PDF, and XML)
Thereof 1,443 with geography defined
and 0 with unknown origin.
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.
Ho Chi Minh City (HCMC) faces severe flood risks from climatic and socio-economic changes,...