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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2025
Research article |
| 25 Aug 2025
| 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
CORRESPONDING AUTHOR
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
Nivedita Sairam
Section Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany
Lukas Schoppa
Section Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany
Le Thanh Sang
Southern Institute of Social Sciences, Ho Chi Minh City, Vietnam
Do Ly Hoai Tan
Southern Institute of Social Sciences, Ho Chi Minh City, Vietnam
Heidi Kreibich
Section Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany
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Annegret H. Thieken, Guilherme Samprogna Mohor, Heidi Kreibich, and Meike Müller
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Valeria Cigala, Giulia Roder, and Heidi Kreibich
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Gustavo Andrei Speckhann, Heidi Kreibich, and Bruno Merz
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Marco Cerri, Max Steinhausen, Heidi Kreibich, and Kai Schröter
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Effective flood management requires information about the potential consequences of flooding. We show how openly accessible data from OpenStreetMap can support the estimation of flood damage for residential buildings. Working with methods of machine learning, the building geometry is used to predict flood damage in combination with information about inundation depth. Our approach makes it easier to transfer models to regions where no detailed data of flood impacts have been observed yet.
Daniela Molinari, Anna Rita Scorzini, Chiara Arrighi, Francesca Carisi, Fabio Castelli, Alessio Domeneghetti, Alice Gallazzi, Marta Galliani, Frédéric Grelot, Patric Kellermann, Heidi Kreibich, Guilherme S. Mohor, Markus Mosimann, Stephanie Natho, Claire Richert, Kai Schroeter, Annegret H. Thieken, Andreas Paul Zischg, and Francesco Ballio
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Flood risk management requires a realistic estimation of flood losses. However, the capacity of available flood damage models to depict real damages is questionable. With a joint effort of eight research groups, the objective of this study was to compare the performances of nine models for the estimation of flood damage to buildings. The comparison provided more objective insights on the transferability of the models and on the reliability of their estimations.
Patric Kellermann, Kai Schröter, Annegret H. Thieken, Sören-Nils Haubrock, and Heidi Kreibich
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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.
Ho Chi Minh City (HCMC) faces severe flood risks from climatic and socio-economic changes,...
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