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: 3,057 (including HTML, PDF, and XML)
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2,774
223
60
3,057
69
59
HTML: 2,774
PDF: 223
XML: 60
Total: 3,057
BibTeX: 69
EndNote: 59
Views and downloads (calculated since 02 Jan 2025)
Cumulative views and downloads
(calculated since 02 Jan 2025)
Total article views: 2,826 (including HTML, PDF, and XML)
HTML
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XML
Total
BibTeX
EndNote
2,547
223
56
2,826
69
59
HTML: 2,547
PDF: 223
XML: 56
Total: 2,826
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)
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227
0
4
231
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Total: 231
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 02 Jan 2025)
Cumulative views and downloads
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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: 3,057 (including HTML, PDF, and XML)
Thereof 2,902 with geography defined
and 155 with unknown origin.
Total article views: 2,826 (including HTML, PDF, and XML)
Thereof 2,686 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,...