Use este identificador para citar ou linkar para este item: 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

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