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Temporal clustering of precipitation for detection of potential landslides

2024Artigo científico Acesso aberto
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dc.contributor.authorBanfi, Fabiola
dc.contributor.authorBevacqua, Emanuele
dc.contributor.authorRivoire, Pauline
dc.contributor.authorOliveira, Sérgio
dc.contributor.authorPinto, Joaquim G.
dc.contributor.authorRamos, Alexandre M.
dc.contributor.authorMichele, Carlo de
dc.date.accessioned2025-01-06T12:44:08Z
dc.date.available2025-01-06T12:44:08Z
dc.date.issued2024
dc.description.abstractLandslides are complex phenomena that cause important impacts in vulnerable areas, including the destruction of infrastructure, environmental damage, and loss of life. The occurrence of landslide events is often triggered by rainfall episodes, single and intense ones or multiple ones occurring in sequence, i.e., clustered in time. Landslide prediction is typically obtained via process-based or empirical thresholds. Here, we develop a new approach that uses information on the temporal clustering of rainfall to detect landslide events and compare it with the use of classical empirical rainfall thresholds. In addition, we evaluate the performance of the two approaches combined together as a case study in the region of Lisbon in Portugal. We consider a dataset that categorizes landslides into shallow and deep events and a review of empirical rainfall thresholds that makes a good benchmark for testing our novel method. We show that the new approach based on temporal clustering overall has a good power of detecting landslide events but has a skill comparable with the classic rainfall threshold method. While there is no clear outperformance of one method, the novel clustering-based method has a higher sensitivity despite a lower precision than the threshold-based method. For all approaches, the potential detection is better for deep landslides than for shallow ones. The results of this study could help to improve the prediction of rainfall-triggered landslides.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBanfi, F., Bevacqua, E., Rivoire, P., Oliveira, S. C., Pinto, J. G., Ramos, A. M., & Michele, C. de (2024). Temporal clustering of precipitation for detection of potential landslides, Natural Hazards and Earth System Sciences, 24, 2689-2704. https://doi.org/10.5194/nhess-24-2689-2024pt_PT
dc.identifier.doi10.5194/nhess-24-2689-2024pt_PT
dc.identifier.eissn1684-9981
dc.identifier.issn1561-8633
dc.identifier.urihttp://hdl.handle.net/10400.5/96852
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherEuropean Geosciences Unionpt_PT
dc.relationRASTOOL (grant agreement no. 101048474)pt_PT
dc.relationEXTREME EVENTS: ARTIFICIAL INTELLIGENCE FOR DETECTION AND ATTRIBUTION
dc.relationEuropean Union Next-GenerationEU (National Recovery and Resilience Plan–NRRP, Mission 4, Component 2, Investment 1.3–D.D. 1243 2/8/2022, PE0000005)pt_PT
dc.relation.publisherversionhttps://nhess.copernicus.org/articles/24/2689/2024/pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNorth-Atlantic oscillationpt_PT
dc.subjectRainfall thresholdspt_PT
dc.subjectLisbon regionpt_PT
dc.subjectWeather typespt_PT
dc.subjectPortugalpt_PT
dc.subjectEventspt_PT
dc.subjectFloodspt_PT
dc.subjectGISpt_PT
dc.titleTemporal clustering of precipitation for detection of potential landslidespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleEXTREME EVENTS: ARTIFICIAL INTELLIGENCE FOR DETECTION AND ATTRIBUTION
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/101003469/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DRI%2FIndia%2F0098%2F2020/PT
oaire.citation.endPage2704pt_PT
oaire.citation.issue8pt_PT
oaire.citation.startPage2689pt_PT
oaire.citation.titleNatural Hazards and Earth System Sciencespt_PT
oaire.citation.volume24pt_PT
oaire.fundingStreamH2020
oaire.fundingStream3599-PPCDT
person.familyNameOliveira
person.givenNameSérgio
person.identifier.ciencia-id1B10-8CE2-1F13
person.identifier.orcid0000-0003-0883-8564
person.identifier.ridM-8412-2016
person.identifier.scopus-author-id24779631800
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationeb79e9a4-db50-4237-8cfa-5ef7a22b243a
relation.isAuthorOfPublication.latestForDiscoveryeb79e9a4-db50-4237-8cfa-5ef7a22b243a
relation.isProjectOfPublicationd07bf264-12f5-4294-b983-0b4245e47511
relation.isProjectOfPublication632a2f53-cbb9-419d-820d-222d63623f18
relation.isProjectOfPublication.latestForDiscoveryd07bf264-12f5-4294-b983-0b4245e47511

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