Data/hora: 20/05/2024 05:00:48

Provedor de dados: 	42	
País: 	Brazil	
Título: 	Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks	
Autores: 	CECíLIO,ROBERTO A.; MOREIRA,MICHEL C.; PEZZOPANE,JOSé EDUARDO M.; PRUSKI,FERNANDO F.; FUKUNAGA,DANILO C.	
Data: 	2013-01-01	
Ano: 	2013	
Palavras-chave: 	Interpolation; Rainfall generator; Soil conservation; Universal soil loss equation.	
Resumo: 	The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI30 and KE&gt;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 EI30 and KE&gt;25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI30 and KE&gt;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.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000401523	
Editor: 	Academia Brasileira de Ciências	
Relação: 	10.1590/0001-3765201398012	
Formato: 	text/html	
Fonte: 	Anais da Academia Brasileira de Ciências  v.85 n.4 2013	
Fonte: 	info:eu-repo/semantics/openAccess