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: 3,053 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,771
222
60
3,053
69
59
HTML: 2,771
PDF: 222
XML: 60
Total: 3,053
BibTeX: 69
EndNote: 59
Views and downloads (calculated since 02 Jan 2025)
Cumulative views and downloads
(calculated since 02 Jan 2025)
Total article views: 2,822 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,544
222
56
2,822
69
59
HTML: 2,544
PDF: 222
XML: 56
Total: 2,822
BibTeX: 69
EndNote: 59
Views and downloads (calculated since 25 Aug 2025)
Cumulative views and downloads
(calculated since 25 Aug 2025)
Total article views: 231 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
227
0
4
231
0
0
HTML: 227
PDF: 0
XML: 4
Total: 231
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: 3,053 (including HTML, PDF, and XML)
Thereof 2,898 with geography defined
and 155 with unknown origin.
Total article views: 2,822 (including HTML, PDF, and XML)
Thereof 2,682 with geography defined
and 140 with unknown origin.
Total article views: 231 (including HTML, PDF, and XML)
Thereof 216 with geography defined
and 15 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,...