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| Registros recuperados : 12 | |
| 3. |  | SANTOS, A. C. P.; MARQUES, P. A. A. Estudo do efeito da irrigacao atraves da utilizacao de tanque classe A na producao do rabanete (Raphanus sativus L.). Horticultura Brasileira, Brasília, DF, v. 19, n. 2, jul. 2001. 1 CD-ROM. Suplemento. Trabalho apresentado no 41. Congresso Brasileiro de Olericultura, 2001. Também publicado em: Horticultura Brasileira, Brasília, DF, v. 19, n. 2, p. 236, jul. 2001. Resumo.| Biblioteca(s): Embrapa Hortaliças. |
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| 10. | | RIBEIRO, V. P.; PADOVANI, C. R.; BALESTIERI, J. A. P. B.; CUNHA, A. S. M.; MARQUES, P. A. A.; DUARTE, S. N.; MACIEL, C. D. Bayesian network for hydrological model: an inference approach. In: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022, Padua, Italy, Proceedings...[S.l.: s.n.], 2022.| Biblioteca(s): Embrapa Pantanal. |
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| 11. | | LENA, B. P.; FOLEGATTI, M. V.; FLUMIGNAN, D. L.; IRMAK, S.; FRANCISCO, J. P.; DIOTTO, A. V.; SANTOS, O. N. A.; ANDRADE, I. P. de S.; FANAYA JUNIOR, E. D.; MARQUES, P. A. A.; BARBOZA JÚNIOR, C. R. A. Water requirement and crop coefficients of young Jatropha curcas L. trees in a subtropical humid environment. Journal of Irrigation and Drainage Engineering, v. 147, n. 7, 04021020, 2021.| Biblioteca(s): Embrapa Agropecuária Oeste. |
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| 12. | | SARAIVA, A. M.; OSÓRIO, F. S.; COLAÇO, A. F.; DRUCKER, D. P.; MENDIONDO, E. M.; CORRÊA, F. E.; SOARES, F. M.; MOLIN, J. P.; BENSO, M. R.; MARQUES, P. A. A.; SILVA, R. F. da; MIRANDA, S. H. G. de; COSTA, W. F.; DELBEM, A. C. B. A inteligência artificial na pesquisa agrícola. Revista USP, n. 141, p. 91-106, abril/maio/junho 2024.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 12 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pantanal. Para informações adicionais entre em contato com cpap.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Pantanal. |
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Data corrente: |
16/11/2022 |
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Data da última atualização: |
17/01/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
RIBEIRO, V. P.; PADOVANI, C. R.; BALESTIERI, J. A. P. B.; CUNHA, A. S. M.; MARQUES, P. A. A.; DUARTE, S. N.; MACIEL, C. D. |
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Afiliação: |
VITOR P. RIBEIRO, Universidade Estadual Paulista; CARLOS ROBERTO PADOVANI, CPAP; JOSE ANTONIO P. BALESTIERI, Universidade Estadual Paulista; ANGELA S. M. CUNHA, Universidade de São Paulo; PATRICIA A. A. MARQUES, Universidade de São Paulo; SERGIO N. DUARTE, Universidade de São Paulo; CARLOS D. MACIEL, Universidade de São Paulo. |
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Título: |
Bayesian network for hydrological model: an inference approach. |
|
Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022, Padua, Italy, Proceedings...[S.l.: s.n.], 2022. |
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DOI: |
10.1109/IJCNN55064.2022.9892468 |
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Idioma: |
Português |
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Conteúdo: |
Abstract: According to the Food and Agriculture Organisation, there are growing concerns about the availability and use of water in agriculture. The hydrological model generates a water balance and the resulting value indicates the amount of available water in a given area. The calculation of the water balance is fundamental for the development of new strategies for the management of water resources. One of its main adversities is the estimation of evapotranspiration, which may be considered a fundamental component. This factor considers climatological variables collected from weather stations that are spread over large areas. However, there are frequent cases of long periods of missing data. We evaluated the performance of a Bayesian Network inference model for estimating evapotranspiration in a large agricultural region in Brazil. To this end, the method considered factors such as accuracy, missing data, and model portability. The results indicate that the model achieves up to 86% accuracy when comparing estimated values to expected values derived from the Penman-Monteith equation. The results show that wind speed and relative humidity are the most critical climatological variables for accurate estimation. |
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Thesagro: |
Balanço Hídrico; Evapotranspiração. |
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Thesaurus NAL: |
Bayesian theory; Evapotranspiration; Water balance. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
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001 2148359
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024 7 $a10.1109/IJCNN55064.2022.9892468$2DOI
100 1 $aRIBEIRO, V. P.
245 $aBayesian network for hydrological model$ban inference approach.$h[electronic resource]
260 $aIn: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022, Padua, Italy, Proceedings...[S.l.: s.n.], 2022.$c2022
520 $aAbstract: According to the Food and Agriculture Organisation, there are growing concerns about the availability and use of water in agriculture. The hydrological model generates a water balance and the resulting value indicates the amount of available water in a given area. The calculation of the water balance is fundamental for the development of new strategies for the management of water resources. One of its main adversities is the estimation of evapotranspiration, which may be considered a fundamental component. This factor considers climatological variables collected from weather stations that are spread over large areas. However, there are frequent cases of long periods of missing data. We evaluated the performance of a Bayesian Network inference model for estimating evapotranspiration in a large agricultural region in Brazil. To this end, the method considered factors such as accuracy, missing data, and model portability. The results indicate that the model achieves up to 86% accuracy when comparing estimated values to expected values derived from the Penman-Monteith equation. The results show that wind speed and relative humidity are the most critical climatological variables for accurate estimation.
650 $aBayesian theory
650 $aEvapotranspiration
650 $aWater balance
650 $aBalanço Hídrico
650 $aEvapotranspiração
700 1 $aPADOVANI, C. R.
700 1 $aBALESTIERI, J. A. P. B.
700 1 $aCUNHA, A. S. M.
700 1 $aMARQUES, P. A. A.
700 1 $aDUARTE, S. N.
700 1 $aMACIEL, C. D.
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