Temporal solar irradiance variability analysis using neural networks. Issue Volume 11:Issue S320(2015) (9th September 2016)

• Record Type: Journal Article
• Authors: Tebabal, Ambelu; Damtie, Baylie; Nigussie, Melessew
• Editors: Kosovichev, A.G.; Hawley, S.L.; Heinzel, P.
• Is Part Of: Proceedings of the International Astronomical Union. Volume 11:Issue S320(2015)
• Page Start: 333
• Page End: 338
• Publication Date: 2016-09-09
• Subjects: Sunspots, -- Neural networks; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921316000296
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: A feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.