inscription
PORTAIL D'INFORMATION GÉOGRAPHIQUE

Application of artificial neural networks in climatology: a case study of sunspot prediction and solar climate trends

Auteurs :
GOPAL, S.
SCUDERI, L.

Description :
Global temperature trends on time scales of years to centuries have recently been shown to be related to volcanic aerosols, carbon dioxide levels, and solar activity. The most visible and well-studied indicators of solar variability are dark areas or sunspots on the surface of the Sun, with sunspot numbers directly related to the level of solar activity. In this paper the AA. show some preliminary findings in using feedforward neural networks for the prediction of peak sunspot cycle amplitude and discuss the climatic implications of the findings.


Type de document :
Article de périodique

Source :
Geographical analysis, issn : 0016-7363, 1995, vol. 27, n°. 1, p. 42-59, Collation : Illustration, Références bibliographiques : 57 ref.

Date :
1995

Editeur :
Pays édition : Etats-Unis, Columbus, OH, Ohio State University Press

Langue :
Anglais
Droits :
Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI)