Your browser doesn't support javascript.
loading
Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility.
Chen, Wei; Lei, Xinxiang; Chakrabortty, Rabin; Chandra Pal, Subodh; Sahana, Mehebub; Janizadeh, Saeid.
Afiliação
  • Chen W; College of Geology & Environment, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China; Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi'an, 710021, China. Electronic address: chenwei.0930@163.com.
  • Lei X; College of Geology & Environment, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China. Electronic address: 19209071003@stu.xust.edu.cn.
  • Chakrabortty R; Department of Geography, The University of Burdwan, West Bengal, India. Electronic address: rabingeo8@gmail.com.
  • Chandra Pal S; Department of Geography, The University of Burdwan, West Bengal, India. Electronic address: geo.subodh@gmail.com.
  • Sahana M; Research Associate, School of Environment, Education & Development, University of Manchester, UK. Electronic address: mehebubsahana@gmail.com.
  • Janizadeh S; Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran, 14115-111, Iran. Electronic address: Janizadeh.saeed@gmail.com.
J Environ Manage ; 284: 112015, 2021 Apr 15.
Article em En | MEDLINE | ID: mdl-33515838
Assuntos
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Conservação dos Recursos Naturais / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: En Revista: J Environ Manage Ano de publicação: 2021 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Conservação dos Recursos Naturais / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: En Revista: J Environ Manage Ano de publicação: 2021 Tipo de documento: Article