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https://locus.ufv.br//handle/123456789/25245
Tipo: | Artigo |
Título: | Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks |
Autor(es): | Cecílio, Roberto A. Moreira, Michel C. Pezzopane, José Eduardo M. Pruski, Fernando F. Fukunaga, Danilo C. |
Abstract: | The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI 30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI 30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI 30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo. |
Palavras-chave: | Interpolation Rainfall generator Soil conservation Universal soil loss equation |
Editor: | Anais da Academia Brasileira de Ciências |
Citação: | v. 85, n. 4, p. 1523-1535, jan. 2013 |
Tipo de Acesso: | Open Access |
URI: | http://dx.doi.org/10.1590/0001-3765201398012 http://www.locus.ufv.br/handle/123456789/25245 |
Data do documento: | Jan-2013 |
Aparece nas coleções: | Engenharia Agrícola - Artigos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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artigo.pdf | artigo | 705,73 kB | Adobe PDF | Visualizar/Abrir |
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