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| 2. |  | FAVA, M. C.; BENSO, M. R.; DELBEM, A. C. B.; SILVA, R. F. da; MENDIONDO, E. M.; PADOVANI, C. R.; GESUALDO, G. C.; SARAIVA, A. M. Automatic spatial rainfall estimation on limited coverage areas. In: IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY, 3., 2021, Trento-Bolzano. Proceedings... [S.l.]: IEEE, 2021. p. 232-237. MetroAgriFor 2021.| Biblioteca(s): Embrapa Pantanal. |
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| 3. | | 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 : 3 | |
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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: |
08/12/2021 |
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Data da última atualização: |
08/12/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
FAVA, M. C.; BENSO, M. R.; DELBEM, A. C. B.; SILVA, R. F. da; MENDIONDO, E. M.; PADOVANI, C. R.; GESUALDO, G. C.; SARAIVA, A. M. |
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Afiliação: |
MARIA CLARA FAVA, Federal University of Viçosa (UFV); MARCOS ROBERTO BENSO, University of São Paulo (USP); ALEXANDRE CLÁUDIO BOTAZZO DELBEM, University of São Paulo (USP); ROBERTO FRAY DA SILVA, University of São Paulo (USP); EDUARDO MARIO MENDIONDO, University of São Paulo (USP); CARLOS ROBERTO PADOVANI, CPAP; GABRIELA CHIQUITO GESUALDO, University of São Paulo (USP); ANTONIO MAURO SARAIVA, University of São Paulo (USP). |
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Título: |
Automatic spatial rainfall estimation on limited coverage areas. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
In: IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY, 3., 2021, Trento-Bolzano. Proceedings... [S.l.]: IEEE, 2021. |
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Páginas: |
p. 232-237. |
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Idioma: |
Português |
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Notas: |
MetroAgriFor 2021. |
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Conteúdo: |
Abstract: Providing accurate rainfall estimation at limited coverage areas is challenging, especially when considering the lack of weather stations maintenance and the existence of missing or incorrect data. Another source of uncertainty related to in situ stations is the need to extrapolate the measures for spatial applications. The Inverse Distance Weighted (IDW) method has been widely used to interpolate rainfall data. When using this method, two hyperparameters need to be defined, the radius of influence and the power factor. However, there are no reference values for these variables in literature for different applications because these are directly related to local features. This study proposes a framework that automatically calculates the rainfall interpolation using IDW and a cross-validation method to find its optimal hyperparameters. It can be directly implemented on any rainfall dataset, regardless of: (i) the amount of data available; (ii) the quality of the area coverage (station density); (iii) the number of weather stations; and (iv) the existence of missing values. Cross-validation is performed for each timestep to consider all the available data for all stations. The method and its symmetric mean absolute percentage error (sMAPE) were evaluated in a case study for the Pantanal Region in Brazil. |
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Thesagro: |
Simulador de Chuva. |
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Thesaurus NAL: |
Estimation; Prediction; Rainfall simulation. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
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100 1 $aFAVA, M. C.
245 $aAutomatic spatial rainfall estimation on limited coverage areas.$h[electronic resource]
260 $aIn: IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY, 3., 2021, Trento-Bolzano. Proceedings... [S.l.]: IEEE$c2021
300 $ap. 232-237.
500 $aMetroAgriFor 2021.
520 $aAbstract: Providing accurate rainfall estimation at limited coverage areas is challenging, especially when considering the lack of weather stations maintenance and the existence of missing or incorrect data. Another source of uncertainty related to in situ stations is the need to extrapolate the measures for spatial applications. The Inverse Distance Weighted (IDW) method has been widely used to interpolate rainfall data. When using this method, two hyperparameters need to be defined, the radius of influence and the power factor. However, there are no reference values for these variables in literature for different applications because these are directly related to local features. This study proposes a framework that automatically calculates the rainfall interpolation using IDW and a cross-validation method to find its optimal hyperparameters. It can be directly implemented on any rainfall dataset, regardless of: (i) the amount of data available; (ii) the quality of the area coverage (station density); (iii) the number of weather stations; and (iv) the existence of missing values. Cross-validation is performed for each timestep to consider all the available data for all stations. The method and its symmetric mean absolute percentage error (sMAPE) were evaluated in a case study for the Pantanal Region in Brazil.
650 $aEstimation
650 $aPrediction
650 $aRainfall simulation
650 $aSimulador de Chuva
700 1 $aBENSO, M. R.
700 1 $aDELBEM, A. C. B.
700 1 $aSILVA, R. F. da
700 1 $aMENDIONDO, E. M.
700 1 $aPADOVANI, C. R.
700 1 $aGESUALDO, G. C.
700 1 $aSARAIVA, A. M.
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