Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction.

Wagenaar, Dennis; Hermawan, Tiaravanni; van den Homberg, Marc J C; Aerts, Jeroen C J H; Kreibich, Heidi; de Moel, Hans; Bouwer, Laurens M

Wagenaar, Dennis; Hermawan, Tiaravanni; van den Homberg, Marc J C; Aerts, Jeroen C J H; Kreibich, Heidi; de Moel, Hans; Bouwer, Laurens M.

Afiliación

Wagenaar D; Deltares, Delft, The Netherlands.
Hermawan T; Institute for Environmental Studies, VU University Amsterdam, The Netherlands.
van den Homberg MJC; Deltares, Delft, The Netherlands.
Aerts JCJH; 510 An initiative of the Netherlands Red Cross, The Hague, The Netherlands.
Kreibich H; Deltares, Delft, The Netherlands.
de Moel H; Institute for Environmental Studies, VU University Amsterdam, The Netherlands.
Bouwer LM; GFZ German Research Centre for Geosciences, Potsdam, Germany.

Risk Anal ; 41(1): 37-55, 2021 01.

Article en En | MEDLINE | ID: mdl-32830337

Palabras clave

damage modeling; disaster risk management; domain adaptation; flood risk management; loss modeling; machine learning; sample selection bias correction

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Risk Anal Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos

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