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    Landslide size matters: a new spatial predictive paradigm

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    Name:
    Landslide_lombardoal2021_eartharxiv.pdf
    Size:
    10.08Mb
    Format:
    PDF
    Description:
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    Type
    Preprint
    Authors
    Lombardo, Luigi cc
    Tanyas, Hakan cc
    Huser, Raphaël cc
    Guzzetti, Fausto cc
    Castro-Camilo, Daniela cc
    KAUST Department
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2021-01-07
    Permanent link to this record
    http://hdl.handle.net/10754/666902
    
    Metadata
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    Abstract
    The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geomorphological community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions.
    Citation
    Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F., & Castro Camilo, D. (2021). Landslide size matters: a new spatial predictive paradigm. doi:10.31223/x5ww3k
    Publisher
    California Digital Library (CDL)
    DOI
    10.31223/x5ww3k
    Additional Links
    http://eartharxiv.org/repository/view/1963/
    ae974a485f413a2113503eed53cd6c53
    10.31223/x5ww3k
    Scopus Count
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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