Mapping landslide susceptibility using data-driven methods
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/10451/27534 |
Resumo: | Most epistemic uncertainty within data-driven landslide susceptibility assessment results from errors in landslide inventories, difficulty in identifying and mapping landslide causes and decisions related with the modelling procedure. In this work we evaluate and discuss differences observed on landslide susceptibility maps resulting from: (i) the selection of the statistical method; (ii) the selection of the terrain mapping unit; and (iii) the selection of the feature type to represent landslides in the model (polygon versus point). The work is performed in a single study area (Silveira Basin - 18.2km(2) - Lisbon Region, Portugal) using a unique database of geo-environmental landslide predisposing factors and an inventory of 82 shallow translational slides. The logistic regression, the discriminant analysis and two versions of the information value were used and we conclude that multivariate statistical methods perform better when computed over heterogeneous terrain units and should be selected to assess landslide susceptibility based on slope terrain units, geo-hydrological terrain units or census terrain units. However, evidence was found that the chosen terrain mapping unit can produce greater differences on final susceptibility results than those resulting from the chosen statistical method for modelling. The landslide susceptibility should be assessed over grid cell terrain units whenever the spatial accuracy of landslide inventory is good. In addition, a single point per landslide proved to be efficient to generate accurate landslide susceptibility maps, providing the landslides are of small size, thus minimizing the possible existence of heterogeneities of predisposing factors within the landslide boundary. Although during last years the ROC curves have been preferred to evaluate the susceptibility model's performance, evidence was found that the model with the highest AUC ROC is not necessarily the best landslide susceptibility model, namely when terrain mapping units are heterogeneous in size and reduced in number. |
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Mapping landslide susceptibility using data-driven methodsLandslide susceptibilityData-driven methodsTerrain mapping unitsValidationUncertaintyMost epistemic uncertainty within data-driven landslide susceptibility assessment results from errors in landslide inventories, difficulty in identifying and mapping landslide causes and decisions related with the modelling procedure. In this work we evaluate and discuss differences observed on landslide susceptibility maps resulting from: (i) the selection of the statistical method; (ii) the selection of the terrain mapping unit; and (iii) the selection of the feature type to represent landslides in the model (polygon versus point). The work is performed in a single study area (Silveira Basin - 18.2km(2) - Lisbon Region, Portugal) using a unique database of geo-environmental landslide predisposing factors and an inventory of 82 shallow translational slides. The logistic regression, the discriminant analysis and two versions of the information value were used and we conclude that multivariate statistical methods perform better when computed over heterogeneous terrain units and should be selected to assess landslide susceptibility based on slope terrain units, geo-hydrological terrain units or census terrain units. However, evidence was found that the chosen terrain mapping unit can produce greater differences on final susceptibility results than those resulting from the chosen statistical method for modelling. The landslide susceptibility should be assessed over grid cell terrain units whenever the spatial accuracy of landslide inventory is good. In addition, a single point per landslide proved to be efficient to generate accurate landslide susceptibility maps, providing the landslides are of small size, thus minimizing the possible existence of heterogeneities of predisposing factors within the landslide boundary. Although during last years the ROC curves have been preferred to evaluate the susceptibility model's performance, evidence was found that the model with the highest AUC ROC is not necessarily the best landslide susceptibility model, namely when terrain mapping units are heterogeneous in size and reduced in number.ElsevierRepositório da Universidade de LisboaZêzere, Joséda Silva Pereira, SusanaMelo, RaquelOliveira, SérgioGarcia, Ricardo A C2017-04-26T13:48:46Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/27534engZêzere, J. L.; Pereira, S.; Melo, R.; Oliveira, S. C.; Garcia, R. A. C. (2017) Mapping landslide susceptibility using data-driven methods. Science of the Total Environment, 589 pp. 250-267.10.1016/j.scitotenv.2017.02.188metadata 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:RCAAP2023-11-08T16:18:30Zoai:repositorio.ul.pt:10451/27534Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:43:55.295289Repositório 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 |
Mapping landslide susceptibility using data-driven methods |
title |
Mapping landslide susceptibility using data-driven methods |
spellingShingle |
Mapping landslide susceptibility using data-driven methods Zêzere, José Landslide susceptibility Data-driven methods Terrain mapping units Validation Uncertainty |
title_short |
Mapping landslide susceptibility using data-driven methods |
title_full |
Mapping landslide susceptibility using data-driven methods |
title_fullStr |
Mapping landslide susceptibility using data-driven methods |
title_full_unstemmed |
Mapping landslide susceptibility using data-driven methods |
title_sort |
Mapping landslide susceptibility using data-driven methods |
author |
Zêzere, José |
author_facet |
Zêzere, José da Silva Pereira, Susana Melo, Raquel Oliveira, Sérgio Garcia, Ricardo A C |
author_role |
author |
author2 |
da Silva Pereira, Susana Melo, Raquel Oliveira, Sérgio Garcia, Ricardo A C |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Zêzere, José da Silva Pereira, Susana Melo, Raquel Oliveira, Sérgio Garcia, Ricardo A C |
dc.subject.por.fl_str_mv |
Landslide susceptibility Data-driven methods Terrain mapping units Validation Uncertainty |
topic |
Landslide susceptibility Data-driven methods Terrain mapping units Validation Uncertainty |
description |
Most epistemic uncertainty within data-driven landslide susceptibility assessment results from errors in landslide inventories, difficulty in identifying and mapping landslide causes and decisions related with the modelling procedure. In this work we evaluate and discuss differences observed on landslide susceptibility maps resulting from: (i) the selection of the statistical method; (ii) the selection of the terrain mapping unit; and (iii) the selection of the feature type to represent landslides in the model (polygon versus point). The work is performed in a single study area (Silveira Basin - 18.2km(2) - Lisbon Region, Portugal) using a unique database of geo-environmental landslide predisposing factors and an inventory of 82 shallow translational slides. The logistic regression, the discriminant analysis and two versions of the information value were used and we conclude that multivariate statistical methods perform better when computed over heterogeneous terrain units and should be selected to assess landslide susceptibility based on slope terrain units, geo-hydrological terrain units or census terrain units. However, evidence was found that the chosen terrain mapping unit can produce greater differences on final susceptibility results than those resulting from the chosen statistical method for modelling. The landslide susceptibility should be assessed over grid cell terrain units whenever the spatial accuracy of landslide inventory is good. In addition, a single point per landslide proved to be efficient to generate accurate landslide susceptibility maps, providing the landslides are of small size, thus minimizing the possible existence of heterogeneities of predisposing factors within the landslide boundary. Although during last years the ROC curves have been preferred to evaluate the susceptibility model's performance, evidence was found that the model with the highest AUC ROC is not necessarily the best landslide susceptibility model, namely when terrain mapping units are heterogeneous in size and reduced in number. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-04-26T13:48:46Z 2017 2017-01-01T00:00:00Z |
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/10451/27534 |
url |
http://hdl.handle.net/10451/27534 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Zêzere, J. L.; Pereira, S.; Melo, R.; Oliveira, S. C.; Garcia, R. A. C. (2017) Mapping landslide susceptibility using data-driven methods. Science of the Total Environment, 589 pp. 250-267. 10.1016/j.scitotenv.2017.02.188 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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 |
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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 |
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