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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: 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)

Classification and Topics

• Classification: Soziale Probleme, Sozialdienste, Versicherungen

Contributors, Places and Time

• Creator: Magnini, Andrea ; Lombardi, Michele ; Persiano, Simone ; Tirri, Antonio ; Lo Conti, Francesco ; Castellarin, Attilio

Further information

• DOI: 10.5194/nhess-22-1469-2022
• URN: urn:nbn:de:101:1-2022042805152170856859
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:20 AM CEST