Machine-learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains
Abstract ∼ 5 km2). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (accuracy: 92 %) relative to univariate ones (accuracy: 84 %), (b) provide accurate predictions of expected inundation depths (determination coefficient ∼ 0.7), and (c) produce encouraging results in extrapolation.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Machine-learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains ; volume:22 ; number:4 ; year:2022 ; pages:1469-1486 ; extent:18
Natural hazards and earth system sciences ; 22, Heft 4 (2022), 1469-1486 (gesamt 18)
- Klassifikation
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Soziale Probleme, Sozialdienste, Versicherungen
- Urheber
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Magnini, Andrea
Lombardi, Michele
Persiano, Simone
Tirri, Antonio
Lo Conti, Francesco
Castellarin, Attilio
- DOI
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10.5194/nhess-22-1469-2022
- URN
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urn:nbn:de:101:1-2022042805152170856859
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:20 MESZ
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Beteiligte
- Magnini, Andrea
- Lombardi, Michele
- Persiano, Simone
- Tirri, Antonio
- Lo Conti, Francesco
- Castellarin, Attilio
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