Bayesian Network for Hydrological Model: an inference approach

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.creatorRibeiro, Vitor P.-
Autor(es): dc.creatorCunha, Angela S.M.-
Autor(es): dc.creatorDuarte, Sergio N.-
Autor(es): dc.creatorPadovani, Carlos R.-
Autor(es): dc.creatorMarques, Patricia A.A.-
Autor(es): dc.creatorMacIel, Carlos D.-
Autor(es): dc.creatorBalestieri, Jose Antonio P.-
Data de aceite: dc.date.accessioned2025-08-21T19:50:34Z-
Data de disponibilização: dc.date.available2025-08-21T19:50:34Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IJCNN55064.2022.9892468-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249308-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249308-
Descrição: dc.descriptionAccording 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.-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionSão Paulo University (USP/IBM/C4AI) Department of Biosystems Engineering, SP-
Descrição: dc.descriptionSão Paulo University (USP) Department of Biosystems Engineering, SP-
Descrição: dc.descriptionGeoprocessing Laboratory Brazilian Agricultural Research Corporation (EMBRAPA), Corumbá, MS-
Descrição: dc.descriptionSão Paulo University (USP) Department of Electrical Engineering, SP-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Neural Networks-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBayesian Inference-
Palavras-chave: dc.subjectBayesian network-
Palavras-chave: dc.subjectEvapotranspiration-
Palavras-chave: dc.subjectWater Balance-
Título: dc.titleBayesian Network for Hydrological Model: an inference approach-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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