| 1. Identificação | |
| Tipo de Referência | Artigo em Revista Científica (Journal Article) |
| Site | plutao.sid.inpe.br |
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identificador | 8JMKD3MGP3W/474NTKP |
| Repositório | sid.inpe.br/plutao/2022/06.15.12.26 (acesso restrito) |
| Última Atualização | 2022:06.20.17.51.41 (UTC) lattes |
| Repositório de Metadados | sid.inpe.br/plutao/2022/06.15.12.26.03 |
| Última Atualização dos Metadados | 2023:01.03.16.52.55 (UTC) administrator |
| DOI | 10.1007/s12517-022-09488-3 |
| ISSN | 1866-7511 |
| Rótulo | lattes: 7712719010541171 9 ZhangQWFLWOGR:2022:ImTrMa |
| Chave de Citação | ZhangQWFLWOGR:2022:ImTrMa |
| Título | Improved tree-based machine learning algorithms combining with bagging strategy for landslide susceptibility modeling  |
| Ano | 2022 |
| Data de Acesso | 12 maio 2024 |
| Tipo de Trabalho | journal article |
| Tipo Secundário | PRE PI |
| Número de Arquivos | 1 |
| Tamanho | 3168 KiB |
| 2. Contextualização | |
| Autor | 1 Zhang, Tingyu 2 Quevedo, Renata Pacheco 3 Wang, Huanyuan 4 Fu, Quan 5 Luo, Dan 6 Wang, Tao 7 Oliveira, Guilherme Garcia de 8 Guasselli, Laurindo Antonio 9 Rennó, Camilo Daleles |
| Identificador de Curriculo | 1 2 3 4 5 6 7 8 9 8JMKD3MGP5W/3C9JGN2 |
| Grupo | 1 2 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 3 4 5 6 7 8 9 DIOTG-CGCT-INPE-MCTI-GOV-BR |
| Afiliação | 1 Key Laboratory of Degraded and Unused Land Consolidation Engineering 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Key Laboratory of Degraded and Unused Land Consolidation Engineering 4 Shaanxi Provincial Land Engineering Construction Group Land Survey Planning, Design Institute Co 5 Shaanxi Provincial Land Engineering Construction Group Land Survey Planning, Design Institute Co 6 Shaanxi Provincial Land Engineering Construction Group Land Survey Planning, Design Institute Co 7 Universidade Federal do Rio Grande do Sul (UFRGS) 8 Universidade Federal do Rio Grande do Sul (UFRGS) 9 Instituto Nacional de Pesquisas Espaciais (INPE) |
| Endereço de e-Mail do Autor | 1 2 renatapquevedo@gmail.com 3 4 5 6 7 8 9 camilo.renno@inpe.br |
| Revista | Arabian Journal of Geosciences |
| Volume | 15 |
| Número | 2 |
| Páginas | 183 |
| Histórico (UTC) | 2022-06-15 12:50:27 :: lattes -> administrator :: 2022 2022-06-17 07:44:40 :: administrator -> lattes :: 2022 2022-06-20 17:51:42 :: lattes -> administrator :: 2022 2023-01-03 16:52:55 :: administrator -> simone :: 2022 |
| 3. Conteúdo e estrutura | |
| É a matriz ou uma cópia? | é a matriz |
| Estágio do Conteúdo | concluido |
| Transferível | 1 |
| Tipo do Conteúdo | External Contribution |
| Tipo de Versão | publisher |
| Palavras-Chave | Landslide susceptibility · Decision tree · Logistic model tree · Reduced error pruning tree · Hybrid models · Bagging strategy |
| Resumo | Landslide is considered one of the most dangerous natural hazards. Reasonable landslide susceptibility mapping can aid decision makers in landslide prevention. For this reason, based on the feld survey data of landslide in Chenggu County, Shaanxi Province, China, 15 conditioning factors (altitude, slope, aspect, plan curvature, profle curvature, SPI, TWI, distance to roads, distance to rivers, distance to faults, rainfall, NDVI, soil, lithology, and land use) were selected and quantifed by the certainty factor index. Then, 184 landslides data were divided into training and validation datasets according to the ratio of 7/3. Based on the GIS platform, three hybrid tree-based models, namely decision tree (DT), logistic model tree (LMT), and reduced error pruning tree (REPT), were established. Additionally, the bagging method was applied to build three baghybrid tree-based models: Bag-DT, Bag-LMT, and Bag-REPT. Finally, the landslide susceptibility maps were produced, and statistical indexes, seed cell area index and the ROC curve, were used for model validation and comparison. The results showed that the bagging method can signifcantly improve the classifcation ability of hybrid models. Furthermore, the BagREPT presented the best performance, with an accuracy value of 92.5%, being a suitable model for landslide susceptibility mapping in the study area. |
| Área | SRE |
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Improved tree-based machine... |
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Improved tree-based machine... |
| Conteúdo da Pasta doc | acessar |
| Conteúdo da Pasta source | não têm arquivos |
| Conteúdo da Pasta agreement | não têm arquivos |
| 4. Condições de acesso e uso | |
| Idioma | en |
| Arquivo Alvo | Zhang2022_Article_ImprovedTree-basedMachineLearn.pdf |
| Grupo de Leitores | administrator lattes |
| Visibilidade | shown |
| Permissão de Leitura | deny from all and allow from 150.163 |
| Permissão de Atualização | não transferida |
| 5. Fontes relacionadas | |
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/46KUATE |
| Lista de Itens Citando | sid.inpe.br/bibdigital/2013/10.18.22.34 2 sid.inpe.br/bibdigital/2022/04.03.22.23 1 |
| Divulgação | WEBSCI; PORTALCAPES; SCOPUS. |
| Acervo Hospedeiro | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notas | |
| Campos Vazios | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn lineage mark mirrorrepository month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url usergroup |
| 7. Controle da descrição | |
| e-Mail (login) | simone |
| atualizar | |