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Combining data-driven models to assess susceptibility of shallow slides failure and run-out

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dc.contributor.authorMelo, Raquel
dc.contributor.authorZêzere, José
dc.contributor.authorRocha, Jorge
dc.contributor.authorOliveira, Sérgio
dc.date.accessioned2020-01-20T12:05:35Z
dc.date.available2020-01-20T12:05:35Z
dc.date.issued2019
dc.description.abstractThis research is focused on the susceptibility assessment of shallow slides by modeling the failure and run-out areas separately. The shallow slides failure is evaluated using a statistical method (logistic regression) and for the run-out assessment, a simple cellular automata model is proposed. The existence of shallow slides inventories occurred in distinct time periods allowed the separation of data into two independent groups (modeling and validation) and the adoption of the temporal criterion for the independent validation. The logistic regression model showed a very good predictive capacity (area under the receiver operating characteristic curve of 0.90), although it may be overestimated, as well as the susceptibility scores obtained. The run-out modeling, using a simple cellular automata model developed for this study, provided good results, with an overlap between the simulation and the real cases of 77%. Lastly, a final shallow slide susceptibility map was constructed including both failure and run-out areas. This work accomplished a combination of low-cost methodology with limited input data that allowed a good performance of the landslide susceptibility assessment and can be easily applied to other regions.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMelo, R., Zêzere, J. L., Rocha, J., & Oliveira, S. C. (2019). Combining data-driven models to assess susceptibility of shallow slides failure and run-out. Landslides, 16(11), 2259-2276. https://doi.org/10.1007/s10346-019-01235-2pt_PT
dc.identifier.doi10.1007/s10346-019-01235-2pt_PT
dc.identifier.issn1612-510X
dc.identifier.issn1612-5118
dc.identifier.urihttp://hdl.handle.net/10451/41272
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationLandslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change
dc.relationCentre of Geographical Studies - University of Lisbon
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s10346-019-01235-2pt_PT
dc.subjectShallow slidespt_PT
dc.subjectSusceptibility to failurept_PT
dc.subjectLogistic regressionpt_PT
dc.subjectRun-out modelingpt_PT
dc.subjectCellular automatapt_PT
dc.titleCombining data-driven models to assess susceptibility of shallow slides failure and run-outpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberPTDC/GES-AMB/30052/2017
oaire.awardNumberUID/GEO/00295/2019
oaire.awardTitleLandslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change
oaire.awardTitleCentre of Geographical Studies - University of Lisbon
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FGES-AMB%2F30052%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FGEO%2F00295%2F2019/PT
oaire.citation.endPage2276pt_PT
oaire.citation.issue11pt_PT
oaire.citation.startPage2259pt_PT
oaire.citation.titleLandslidespt_PT
oaire.citation.volume16pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMelo
person.familyNameZêzere
person.familyNameRocha
person.familyNameOliveira
person.givenNameRaquel
person.givenNameJosé Luís
person.givenNameJorge
person.givenNameSérgio
person.identifierH-9956-2013
person.identifier0000000069085031
person.identifier.ciencia-idED1B-82B2-9E4F
person.identifier.ciencia-id511D-EE6B-47E3
person.identifier.ciencia-idEC15-76DC-9B96
person.identifier.ciencia-id1B10-8CE2-1F13
person.identifier.orcid0000-0002-8111-8777
person.identifier.orcid0000-0002-3953-673X
person.identifier.orcid0000-0002-7228-6330
person.identifier.orcid0000-0003-0883-8564
person.identifier.ridO-1282-2018
person.identifier.ridF-3185-2017
person.identifier.ridM-8412-2016
person.identifier.scopus-author-id54893395400
person.identifier.scopus-author-id6507109389
person.identifier.scopus-author-id56428061000
person.identifier.scopus-author-id24779631800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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