A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates (2023)
- Authors:
- USP affiliated authors: MACIEL, CARLOS DIAS - EESC ; ACHCAR, JORGE ALBERTO - FMRP ; MARQUES, PATRICIA ANGÉLICA ALVES - ESALQ ; DESUÓ NETO, LUIZ - EESC
- Unidades: EESC; FMRP; ESALQ
- DOI: 10.3390/agronomy13122970
- Subjects: BALANÇO HÍDRICO; EVAPOTRANSPIRAÇÃO; INFERÊNCIA BAYESIANA; INTELIGÊNCIA ARTIFICIAL; IRRIGAÇÃO; MODELOS PARA PROCESSOS ESTOCÁSTICOS; SISTEMAS DE APOIO À DECISÃO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
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- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
RIBEIRO, Vitor P et al. A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates. Agronomy, v. 13, p. 1-28, 2023Tradução . . Disponível em: https://dx.doi.org/10.3390/agronomy13122970. Acesso em: 02 jun. 2024. -
APA
Ribeiro, V. P., Desuó Neto, L., Marques, P. A. A., Achcar, J. A., Junqueira, A. M., Chinatto Junior, A. W., et al. (2023). A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates. Agronomy, 13, 1-28. doi:10.3390/agronomy13122970 -
NLM
Ribeiro VP, Desuó Neto L, Marques PAA, Achcar JA, Junqueira AM, Chinatto Junior AW, Junqueira CCM, Maciel CD, Balestieri JAP. A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates [Internet]. Agronomy. 2023 ; 13 1-28.[citado 2024 jun. 02 ] Available from: https://dx.doi.org/10.3390/agronomy13122970 -
Vancouver
Ribeiro VP, Desuó Neto L, Marques PAA, Achcar JA, Junqueira AM, Chinatto Junior AW, Junqueira CCM, Maciel CD, Balestieri JAP. A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates [Internet]. Agronomy. 2023 ; 13 1-28.[citado 2024 jun. 02 ] Available from: https://dx.doi.org/10.3390/agronomy13122970 - Bayesian versus neural network analysis of algae data population: a new method to predict and analyse cause and effect
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Informações sobre o DOI: 10.3390/agronomy13122970 (Fonte: oaDOI API)
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