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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/4859QG2
Repositóriosid.inpe.br/mtc-m21d/2022/12.01.18.09   (acesso restrito)
Última Atualização2022:12.01.18.09.56 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/12.01.18.09.56
Última Atualização dos Metadados2023:01.03.16.46.26 (UTC) administrator
DOI10.1007/s11069-022-05520-7
ISSN0921-030X
Chave de CitaçãoZhangFLLWHQCL:2022:MoLaSu
TítuloModeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest
Ano2022
MêsDec.
Data de Acesso13 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho18522 KiB
2. Contextualização
Autor1 Zhang, Tingyu Y.
2 Fu, Quan
3 Li, Chao
4 Liu, Fangfang
5 Wang, Huanyuan
6 Han, Ling
7 Quevedo, Renata Pacheco
8 Chen, Tianqing
9 Lei, Na
Grupo1
2
3
4
5
6
7 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
Afiliação1 Key Laboratory of Degraded and Unused Land Consolidation Engineering
2 Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute Co. Ltd
3 Shaanxi Land Engineering Construction Group Co. Ltd
4 Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute Co. Ltd
5 Chang’an University
6 Chang’an University
7 Instituto Nacional de Pesquisas Espaciais (INPE)
8 Key Laboratory of Degraded and Unused Land Consolidation Engineering
9 Key Laboratory of Degraded and Unused Land Consolidation Engineering
Endereço de e-Mail do Autor1
2
3
4
5 whysxdj2021@163.com
6
7 renatapquevedo@gmail.com
RevistaNatural Hazards
Volume114
Número3
Páginas3327-3358
Nota SecundáriaA1_ENGENHARIAS_I A2_GEOGRAFIA A2_CIÊNCIAS_AGRÁRIAS_I B1_INTERDISCIPLINAR B1_GEOCIÊNCIAS
Histórico (UTC)2022-12-01 18:09:56 :: simone -> administrator ::
2022-12-01 18:09:57 :: administrator -> simone :: 2022
2022-12-01 18:11:44 :: simone -> administrator :: 2022
2023-01-03 16:46:26 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveLandslide susceptibility
Kernel logistic regression
Fuzzy unordered rule induction algorithm
Systematically developed forest of multiple trees
Random forest
ResumoThis paper introduces four advanced intelligent algorithms, namely kernel logistic regression, fuzzy unordered rule induction algorithm, systematically developed forest of multiple decision trees and random forest (RF), to perform the landslide susceptibility mapping in Jian'ge County, China, as well as well study of the connection between landslide occurrence and regional geo-environment characteristics. To start with, 262 landslide events were determined, and the proportion of randomly generated training data is 70%, while the proportion of randomly generated validation data is 30%, respectively. Then, through the comprehensive consideration of local geo-environment characteristics and relevant studies, fifteen conditioning factors were prepared, such as slope angle, slope aspect, altitude, profile curvature, plan curvature, sediment transport index, topographic wetness index, stream power index, distance to rivers, distance to roads, distance to lineaments, soil, land use, lithology and NDVI. Next, frequency ratio model was utilized to identify the corresponding relations for conditioning factors and landslides distribution. In addition, four data mining techniques were conducted to implement the landslide susceptibility research and generated landslide susceptibility maps. In order to examine and compare model performance, receiver operating characteristic curve was brought for judging accuracy of those four models. Finally, the results indicated that a traditional model, namely RF model, acquired the highest AUC value (0.859). Last but gained a lot of attention, the results can provide references for land use management and landslide prevention.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Modeling landslide susceptibility...
Arranjo 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Modeling landslide susceptibility...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvos11069-022-05520-7.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft12
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
Lista de Itens Citandosid.inpe.br/bibdigital/2013/10.18.22.34 4
sid.inpe.br/bibdigital/2022/04.03.22.23 2
DivulgaçãoWEBSCI; PORTALCAPES; SCOPUS.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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