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: 2,959 (including HTML, PDF, and XML)
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2,709
198
52
2,959
67
56
HTML: 2,709
PDF: 198
XML: 52
Total: 2,959
BibTeX: 67
EndNote: 56
Views and downloads (calculated since 02 Jan 2025)
Cumulative views and downloads
(calculated since 02 Jan 2025)
Total article views: 2,729 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,483
198
48
2,729
67
56
HTML: 2,483
PDF: 198
XML: 48
Total: 2,729
BibTeX: 67
EndNote: 56
Views and downloads (calculated since 25 Aug 2025)
Cumulative views and downloads
(calculated since 25 Aug 2025)
Total article views: 230 (including HTML, PDF, and XML)
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226
0
4
230
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Total: 230
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: 2,959 (including HTML, PDF, and XML)
Thereof 2,822 with geography defined
and 137 with unknown origin.
Total article views: 2,729 (including HTML, PDF, and XML)
Thereof 2,606 with geography defined
and 123 with unknown origin.
Total article views: 230 (including HTML, PDF, and XML)
Thereof 216 with geography defined
and 14 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,...