Interpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental US

dc.contributor.authorVeigel, Nadja
dc.contributor.authorKreibich, Heidi
dc.contributor.authorCominola, Andrea
dc.date.accessioned2023-11-08T13:07:53Z
dc.date.available2023-11-08T13:07:53Z
dc.date.issued2023-09-22
dc.date.updated2023-10-16T08:09:27Z
dc.description.abstractFloods cause average annual losses of more than US$30 billion in the US and are estimated to significantly increase due to global change. Flood resilience, which currently differs strongly between socio-economic groups, needs to be substantially improved by proactive adaptive measures, such as timely purchase of flood insurance. Yet, knowledge about the state and uptake of private adaptation and its drivers is so far scarce and fragmented. Based on interpretable machine learning and large insurance and socio-economic open data sets covering the whole continental US we reveal that flood insurance purchase is characterized by reactive behavior after severe flood events. However, we observe that the Community Rating System helps overcome this behavior by effectively fostering proactive insurance purchase, irrespective of socio-economic backgrounds in the communities. Thus, we recommend developing additional targeted measures to help overcome existing inequalities, for example, by providing special incentives to the most vulnerable and exposed communities.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2023
dc.identifier.eissn2328-4277
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/20254
dc.identifier.urihttps://doi.org/10.14279/depositonce-19052
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc300 Sozialwissenschaften::330 Wirtschaft::330 Wirtschaft
dc.subject.ddc500 Naturwissenschaften und Mathematik::550 Geowissenschaften, Geologie::551 Geologie, Hydrologie, Meteorologie
dc.subject.otherFEMA
dc.subject.othermachine learning
dc.subject.otherflood insurance
dc.subject.otherhuman behavior
dc.subject.otherflood resilience
dc.titleInterpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental USen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumbere2023EF003571
dcterms.bibliographicCitation.doi10.1029/2023EF003571
dcterms.bibliographicCitation.issue9
dcterms.bibliographicCitation.journaltitleEarth's Futureen
dcterms.bibliographicCitation.originalpublishernameWiley
dcterms.bibliographicCitation.originalpublisherplaceNew York, NY
dcterms.bibliographicCitation.pageend
dcterms.bibliographicCitation.pagestart
dcterms.bibliographicCitation.volume11
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme::Inst. Strömungsmechanik und Technische Akustik (ISTA)::FG Smart Water Networks
tub.publisher.universityorinstitutionTechnische Universität Berlin

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