Bayesian Network for Hydrological Model: an inference approach
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN55064.2022.9892468 http://hdl.handle.net/11449/249308 |
Resumo: | 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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Repositório Institucional da UNESP |
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Bayesian Network for Hydrological Model: an inference approachBayesian InferenceBayesian networkEvapotranspirationWater BalanceAccording 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.School of Engineering São Paulo State University (UNESP), SPSão Paulo University (USP/IBM/C4AI) Department of Biosystems Engineering, SPSão Paulo University (USP) Department of Biosystems Engineering, SPGeoprocessing Laboratory Brazilian Agricultural Research Corporation (EMBRAPA), Corumbá, MSSão Paulo University (USP) Department of Electrical Engineering, SPSchool of Engineering São Paulo State University (UNESP), SPUniversidade Estadual Paulista (UNESP)Universidade de São Paulo (USP)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Ribeiro, Vitor P. [UNESP]Cunha, Angela S.M.Duarte, Sergio N.Padovani, Carlos R.Marques, Patricia A.A.MacIel, Carlos D.Balestieri, Jose Antonio P. [UNESP]2023-07-29T15:12:30Z2023-07-29T15:12:30Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/IJCNN55064.2022.9892468Proceedings of the International Joint Conference on Neural Networks, v. 2022-July.http://hdl.handle.net/11449/24930810.1109/IJCNN55064.2022.98924682-s2.0-85140720345Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-07-01T19:30:12Zoai:repositorio.unesp.br:11449/249308Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:33:14.798014Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Bayesian Network for Hydrological Model: an inference approach |
title |
Bayesian Network for Hydrological Model: an inference approach |
spellingShingle |
Bayesian Network for Hydrological Model: an inference approach Ribeiro, Vitor P. [UNESP] Bayesian Inference Bayesian network Evapotranspiration Water Balance |
title_short |
Bayesian Network for Hydrological Model: an inference approach |
title_full |
Bayesian Network for Hydrological Model: an inference approach |
title_fullStr |
Bayesian Network for Hydrological Model: an inference approach |
title_full_unstemmed |
Bayesian Network for Hydrological Model: an inference approach |
title_sort |
Bayesian Network for Hydrological Model: an inference approach |
author |
Ribeiro, Vitor P. [UNESP] |
author_facet |
Ribeiro, Vitor P. [UNESP] Cunha, Angela S.M. Duarte, Sergio N. Padovani, Carlos R. Marques, Patricia A.A. MacIel, Carlos D. Balestieri, Jose Antonio P. [UNESP] |
author_role |
author |
author2 |
Cunha, Angela S.M. Duarte, Sergio N. Padovani, Carlos R. Marques, Patricia A.A. MacIel, Carlos D. Balestieri, Jose Antonio P. [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade de São Paulo (USP) Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) |
dc.contributor.author.fl_str_mv |
Ribeiro, Vitor P. [UNESP] Cunha, Angela S.M. Duarte, Sergio N. Padovani, Carlos R. Marques, Patricia A.A. MacIel, Carlos D. Balestieri, Jose Antonio P. [UNESP] |
dc.subject.por.fl_str_mv |
Bayesian Inference Bayesian network Evapotranspiration Water Balance |
topic |
Bayesian Inference Bayesian network Evapotranspiration Water Balance |
description |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-07-29T15:12:30Z 2023-07-29T15:12:30Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IJCNN55064.2022.9892468 Proceedings of the International Joint Conference on Neural Networks, v. 2022-July. http://hdl.handle.net/11449/249308 10.1109/IJCNN55064.2022.9892468 2-s2.0-85140720345 |
url |
http://dx.doi.org/10.1109/IJCNN55064.2022.9892468 http://hdl.handle.net/11449/249308 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, v. 2022-July. 10.1109/IJCNN55064.2022.9892468 2-s2.0-85140720345 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129085097902080 |