Combining data-driven models to assess susceptibility of shallow slides failure and run-out
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/41272 |
Resumo: | This 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. |
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Combining data-driven models to assess susceptibility of shallow slides failure and run-outShallow slidesSusceptibility to failureLogistic regressionRun-out modelingCellular automataThis 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.SpringerRepositório da Universidade de LisboaMelo, RaquelZêzere, JoséRocha, JorgeOliveira, Sérgio2020-01-20T12:05:35Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/41272engMelo, 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), 2259-2276.1612-510X10.1007/s10346-019-01235-2metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:40:34Zoai:repositorio.ul.pt:10451/41272Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:54:33.773593Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out |
title |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out |
spellingShingle |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out Melo, Raquel Shallow slides Susceptibility to failure Logistic regression Run-out modeling Cellular automata |
title_short |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out |
title_full |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out |
title_fullStr |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out |
title_full_unstemmed |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out |
title_sort |
Combining data-driven models to assess susceptibility of shallow slides failure and run-out |
author |
Melo, Raquel |
author_facet |
Melo, Raquel Zêzere, José Rocha, Jorge Oliveira, Sérgio |
author_role |
author |
author2 |
Zêzere, José Rocha, Jorge Oliveira, Sérgio |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Melo, Raquel Zêzere, José Rocha, Jorge Oliveira, Sérgio |
dc.subject.por.fl_str_mv |
Shallow slides Susceptibility to failure Logistic regression Run-out modeling Cellular automata |
topic |
Shallow slides Susceptibility to failure Logistic regression Run-out modeling Cellular automata |
description |
This 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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2020-01-20T12:05:35Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/41272 |
url |
http://hdl.handle.net/10451/41272 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Melo, 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. 1612-510X 10.1007/s10346-019-01235-2 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134485410742272 |