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Registros recuperados : 14 | |
9. | | BOKULICH, N.; HWANG, C. F.; LIU, S.; MILLS, K. L. B.; MILLS, D. A. Profiling the yeast communities of wine fermentations using terminal restriction fragment length polymorphism analysis. American Journal of Enology and Viticulture, Reedley, v. 63, n. 2, p. 185-194, 2012. Biblioteca(s): Embrapa Semiárido. |
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11. | | ANDERSON, J. A.; STACK, R. W.; LIU, S.; WALDRON, B. L.; FJELD, A. D.; COYNE, C.; MORENO-SEVILLA, B.; MITCHELL FETCH, J.; SONG, Q. J.; CREGAN, P. B.; FROHBERG, R. C. DNA markers for fusarium head blight resistance QTLS in two wheat populations. Theoretical and Applied Genetics, v. 102, p. 1164-1168, 2001. Biblioteca(s): Embrapa Trigo. |
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12. | | LACAPE, J. M.; GAWRYSIAK, G.; CAO, T. V.; VIOT, C.; LIEWELLYN, D.; LIU, S.; JACOBS, J.; BECKER, D.; BARROSO, P. A. V.; ASSUNCAO, J. H. de; PALAI, O.; GEORGES, S.; JEAN, J.; GIBAND, M. Mapping QTLs for traits related to phenology, morphology and yield components in an inter-specific Gossypium hirsutum × G. barbadense cotton RIL population. Field Crops Research, v. 144, p. 256?267, 2013. Biblioteca(s): Embrapa Algodão. |
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13. | | LACAPE, J.- M.; LLEWELLYN, D.; JACOBS, J.; ARIOLI, T.; BECKER, D.; CALHOUN, S.; AL-GHAZI, Y.; LIU, S.; PALAI, O.; GEORGES, S.; GIBAND, M.; ASSUNÇÃO, H. de; BARROSO, P. A. V.; CLAVERIE, M.; GAWRYZIAK, G.; JEAN, J.; VIALLE, M.; VIOT, C. Meta-analysis of cotton fiber quality QTLs across diverse environments in a Gossypium hirsutum x G. barbadense RIL population. BMC Plant Biology, v.10, n. 132, p. 1-24, 2010. Biblioteca(s): Embrapa Algodão. |
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14. | | YOUNG, N. D.; JEX, A. R.; LI, B.; LIU, S.; YANG, L.; XIONG, Z.; LI, Y.; CANTACESSI, C.; HALL, R. S.; XU, X.; CHEN, F.; WU, X.; ZERLOTINI, A.; OLIVEIRA, G.; HOFMANN, A.; ZHANG, G.; FANG, X.; KANG, Y.; CAMPBELL, B. E.; LOUKAS, A.; RANGANATHAN, S.; ROLLINSON, D.; RINALDI, G.; BRINDLEY, P. J.; YANG, H.; WANG, J.; WANG, J.; GASSER, R. B. Whole-genome sequence of Schistosoma haematobium. Nature Genetics, v. 44, n. 2, p. 221-225, Feb. 2012. Biblioteca(s): Embrapa Agricultura Digital. |
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Registros recuperados : 14 | |
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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
Biblioteca(s): |
Embrapa Pantanal. |
Data corrente: |
08/12/2021 |
Data da última atualização: |
08/12/2021 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
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. |
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). |
Título: |
Automatic spatial rainfall estimation on limited coverage areas. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
In: IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY, 3., 2021, Trento-Bolzano. Proceedings... [S.l.]: IEEE, 2021. |
Páginas: |
p. 232-237. |
Idioma: |
Português |
Notas: |
MetroAgriFor 2021. |
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. |
Thesagro: |
Simulador de Chuva. |
Thesaurus NAL: |
Estimation; Prediction; Rainfall simulation. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 02164nam a2200265 a 4500 001 2137346 005 2021-12-08 008 2021 bl uuuu u00u1 u #d 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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