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Susceptibility assessment 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.accessioned2020-01-20T14:47:26Z
dc.date.available2020-01-20T14:47:26Z
dc.date.issued2019
dc.description.abstractThe research is focused on the susceptibility assessment of shallow slides in the region north of Lisbon (Portugal), by modelling the failure and run-out areas separately. The shallow slides failure is evaluated using a statistical method (logistic regression). The existence of shallow slides inventories occurred in distinct periods allowed the separation of data into two independent groups (training and validation) and the adoption of the temporal criterion for the independent validation. The latter revealed an Area Under the Receiver Operating Characteristic curve of 0.90, which reflects a very good predictive capacity of the logistic regression model. For the run-out assessment, a simple cellular automata model is implemented through the following sequential steps: a) pre-processing and establishment of transition rules; b) integration of variables; and c) temporal indexing and simulation. The pre-processing step includes the creation of a database with the modelling inputs. The transition rules are directly related with the motion of the displaced mass. In this context, the likely traveling directions are identified, both horizontally and vertically. The integration of transition rules is performed using the algorithm Path Distance, from ESRI. For the temporal indexing, we use the Markov chains analysis to estimate a transition area matrix, which records the number of cells that is expected to change location over a specified time. The last stage refers to the cellular automata model simulation, i.e. to the spatial distribution of the landslide displaced mass. The run-out modelling, using the cellular automata model proposed, 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.urihttp://hdl.handle.net/10451/41293
dc.language.isoengpt_PT
dc.peerreviewednopt_PT
dc.publisherEuropean Geosciences Unionpt_PT
dc.relationLandslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change
dc.relationMODELAÇÃO DINÂMICA DA PERIGOSIDADE A MOVIMENTOS DE VERTENTE E DESENVOLVIMENTO DE UM PROTÓTIPO DE SISTEMA DE ALERTA À ESCALA REGIONAL MOVALERT
dc.relationCentre of Geographical Studies - University of Lisbon
dc.relation.publisherversionhttps://meetingorganizer.copernicus.org/EGU2019/EGU2019-2983.pdfpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectShallow slidespt_PT
dc.subjectSusceptibility assessmentpt_PT
dc.titleSusceptibility assessment of shallow slides failure and run-outpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleLandslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change
oaire.awardTitleMODELAÇÃO DINÂMICA DA PERIGOSIDADE A MOVIMENTOS DE VERTENTE E DESENVOLVIMENTO DE UM PROTÓTIPO DE SISTEMA DE ALERTA À ESCALA REGIONAL MOVALERT
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/OE/SFRH%2FBPD%2F85827%2F2012/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FGEO%2F00295%2F2019/PT
oaire.citation.titleGeophysical Research Abstractspt_PT
oaire.citation.volume21pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStreamOE
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.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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