Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

Santos, T. Soares dos; Mendes, David; Torres, R. Rodrigues

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

Página do item simplificado Estatísticas

dc.contributor.author	Santos, T. Soares dos
dc.contributor.author	Mendes, David
dc.contributor.author	Torres, R. Rodrigues
dc.date.accessioned	2020-05-30T13:55:04Z
dc.date.available	2020-05-30T13:55:04Z
dc.date.issued	2016
dc.description.resumo	Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970–1999) and two scenarios (RCP 2.6 and 8.5; 2070–2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability	pt_BR
dc.identifier.citation	SANTOS, T. Soares dos ; Mendes, D. ; TORRES, R. Rodrigues. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America. Nonlinear processes in Geophysics, v. 23, p. 13-20, 2016. Disponível em: https://www.nonlin-processes-geophys.net/23/13/2016/npg-23-13-2016.pdf. Acesso em: 29 Maio 2020. https://doi.org/10.5194/npg-23-13-2016	pt_BR
dc.identifier.doi	https://doi.org/10.5194/npg-23-13-2016
dc.identifier.uri	https://repositorio.ufrn.br/jspui/handle/123456789/29096
dc.language	en	pt_BR
dc.publisher	Nonlinear Processes Geophysics	pt_BR
dc.rights	Attribution 3.0 Brazil	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/br/	*
dc.subject	Application of artificial neural networks	pt_BR
dc.title	Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America	pt_BR
dc.type	article	pt_BR