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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. |
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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. |
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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: |
LEADER 02050nam a2200253 a 4500
001 2148359
005 2023-01-17
008 2022 bl uuuu u00u1 u #d
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 $aIn: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022, Padua, Italy, Proceedings...[S.l.: s.n.]$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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| 114. |  | MOREIRA, J. C. S.; SOUSA, C. N. A. de; GOMES, E. P.; MEDEIROS, M. C. Informações sobre as cultivares de trigo recomendadas para plantio em 1991, no Rio Grande do Sul. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 33, p. 521-535, maio 1998.| Biblioteca(s): Embrapa Trigo; Embrapa Unidades Centrais. |
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