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Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)

dc.contributor.authorNocentini, Nicola
dc.contributor.authorRosi, Ascanio
dc.contributor.authorPiciullo, Luca
dc.contributor.authorLiu, Zhongqiang
dc.contributor.authorSegoni, Samuele
dc.contributor.authorFanti, Riccardo
dc.date.accessioned2024-07-16T13:12:16Z
dc.date.available2024-07-16T13:12:16Z
dc.date.created2024-06-25T09:04:20Z
dc.date.issued2024
dc.identifier.citationLandslides. Journal of the International Consortium on Landslides. 2024, .
dc.identifier.issn1612-510X
dc.identifier.urihttps://hdl.handle.net/11250/3141526
dc.language.isoeng
dc.titleRegional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)
dc.title.alternativeRegional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.pagenumber0
dc.source.journalLandslides. Journal of the International Consortium on Landslides
dc.identifier.doi10.1007/s10346-024-02287-9
dc.identifier.cristin2278554
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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