Data/hora: 12/06/2024 07:17:36

Provedor de dados: 	70	
País: 	Brazil	
Título: 	Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network	
Autores: 	Carvalho,Daniel F. de; Khoury Júnior,Joseph K.; Varella,Carlos A. A.; Giori,Jacqueline Z.; Machado,Roriz L.	
Data: 	2012-02-01	
Ano: 	2012	
Palavras-chave: 	Geographic information system; Interpolation methods; Soil conservation.	
Resumo: 	The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000100020	
Editor: 	Associação Brasileira de Engenharia Agrícola	
Relação: 	10.1590/S0100-69162012000100020	
Formato: 	text/html	
Fonte: 	Engenharia Agrícola  v.32 n.1 2012	
Fonte: 	info:eu-repo/semantics/openAccess