Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble (2021)
- Authors:
- Autor USP: BAZAME, HELIZANI COUTO - ESALQ
- Unidade: ESALQ
- DOI: 10.1007/s00477-021-01980-8
- Subjects: HIDROLOGIA; INFERÊNCIA ESTATÍSTICA; MÉTODO DE MONTE CARLO; REDES NEURAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2021
- Source:
- Título do periódico: Stochastic Environmental Research and Risk Assessment
- ISSN: 1436-3259
- Volume/Número/Paginação/Ano: online, p. 1-17, 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ALTHOFF, Daniel e RODRIGUES, Lineu Neiva e BAZAME, Helizani Couto. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stochastic Environmental Research and Risk Assessment, p. 1-17, 2021Tradução . . Disponível em: https://doi.org/10.1007/s00477-021-01980-8. Acesso em: 02 jun. 2024. -
APA
Althoff, D., Rodrigues, L. N., & Bazame, H. C. (2021). Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stochastic Environmental Research and Risk Assessment, 1-17. doi:10.1007/s00477-021-01980-8 -
NLM
Althoff D, Rodrigues LN, Bazame HC. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble [Internet]. Stochastic Environmental Research and Risk Assessment. 2021 ; 1-17.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1007/s00477-021-01980-8 -
Vancouver
Althoff D, Rodrigues LN, Bazame HC. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble [Internet]. Stochastic Environmental Research and Risk Assessment. 2021 ; 1-17.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1007/s00477-021-01980-8 - Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset
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Informações sobre o DOI: 10.1007/s00477-021-01980-8 (Fonte: oaDOI API)
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