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Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal

2024Journal article Open access
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dc.contributor.authorVillaça, Caio
dc.contributor.authorSantos, Pedro Pinto
dc.contributor.authorZêzere, José
dc.date.accessioned2024-06-25T14:55:04Z
dc.date.available2024-06-25T14:55:04Z
dc.date.issued2024
dc.description.abstractRainfall-triggered landslides pose a significant threat to both infrastructure and human lives, making it crucial to comprehend the factors that contribute to their occurrence. Specifically, understanding the relationship between these factors and the amount of rain that is necessary for triggering such events is essential for effective prediction and mitigation strategies. To address this issue, our study proposes a statistical modelling approach using machine learning, specifically the Random Forest algorithm, to investigate the connection between landslide predisposing factors and the daily rainfall intensity threshold necessary for the initiation of shallow landslides in Portugal. By leveraging a comprehensive dataset comprising historical landslide events, associated critical rainfall, and ten distinct landslide predisposing factors, we developed several models and used cross-validation technique to evaluate their performance. Our findings demonstrate that the Random Forest model effectively captures a relationship among landslide predisposing factors, critical daily rainfall intensity, and landslide occurrences. The models exhibit a satisfactory accuracy in assessing the spatial variation of critical daily rainfall intensity based on the predisposing factors, with a mean absolute percentage error (MAPE) of around 17%. Furthermore, the models provide valuable insights into the relative importance of various predisposing factors in landslide triggering, highlighting the significance of each factor. It was found that it takes higher rainfall intensity to trigger shallow landslides in the north region of Portugal when considering critical rainfall events of 3 and 13 days. Slope aspect, slope angle, and clay content in the soil are among the main predisposing factors used for defining the spatial variation of the daily rainfall intensity threshold.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.citationVillaça, C., Santos, P., & Zêzere, J. (2024). Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal. Landslides, 21(9), 2119–2133. https://doi.org/10.1007/s10346-024-02284-ypt_PT
dc.identifier.doi10.1007/s10346-024-02284-ypt_PT
dc.identifier.eissn1612-5118
dc.identifier.issn1612-510X
dc.identifier.urihttp://hdl.handle.net/10451/65132
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationUIDB/00295/2020pt_PT
dc.relationUIDP/00295/2020pt_PT
dc.relation2022.14473.BDpt_PT
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s10346-024-02284-ypt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCritical rainfall intensitypt_PT
dc.subjectPredisposing factorspt_PT
dc.subjectShallow landslidept_PT
dc.subjectRandom Forestpt_PT
dc.titleModelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage2133
oaire.citation.issue9
oaire.citation.startPage2119
oaire.citation.titleLandslidespt_PT
oaire.citation.volume21
person.familyNameVillaça
person.familyNameSantos
person.familyNameZêzere
person.givenNameCaio
person.givenNamePedro Pinto
person.givenNameJosé Luís
person.identifierH-9956-2013
person.identifier.ciencia-idC31F-D8E5-E5E3
person.identifier.ciencia-id511D-EE6B-47E3
person.identifier.orcid0000-0003-1230-6341
person.identifier.orcid0000-0001-9785-0180
person.identifier.orcid0000-0002-3953-673X
person.identifier.ridD-7076-2014
person.identifier.scopus-author-id56499523100
person.identifier.scopus-author-id6507109389
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication632ee824-ddbf-4233-b7dc-3870bd1e5887
relation.isAuthorOfPublicationdd3ba412-bd7e-43d2-9b96-958985bd21f0
relation.isAuthorOfPublicationa49d5ad5-533a-4973-b8c6-a4f201b1cf62
relation.isAuthorOfPublication.latestForDiscoverya49d5ad5-533a-4973-b8c6-a4f201b1cf62

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