Correlation between sunspots and interplanetary shocks measured by ACE during 1998-2014 and some estimations for the 22nd solar cycle and the years between 2015 and 2018 with artificial neural network using the Cavus 2013 model

ÇAVUŞ H., Araz G., Coban G. C., Raheem A., Karafistan A. I.

ADVANCES IN SPACE RESEARCH, cilt.65, sa.3, ss.1035-1047, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 65 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.asr.2019.09.056
  • Dergi Adı: ADVANCES IN SPACE RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Artic & Antarctic Regions, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1035-1047
  • Anahtar Kelimeler: Interplanetary shocks, Solar activity, Sunspots, Artificial Neural Network, Estimation, GEOMAGNETIC-ACTIVITY, MAGNETIC CLOUDS, DECEMBER 13, VISCOSITY, WIND, WAVES, DRIVERS, NUMBER
  • Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

The Advanced Composition Explorer (ACE) spacecraft has measured 235 solar-based interplanetary (IP) shock waves between the years of 1998-2014. These were composed of 203 fast forward (FF), 6 slow forward (SF), 21 fast reverse (FR) and 5 slow reverse (SR) type shocks. These data can be obtained from the Interplanetary Shock Database of Harvard-Smithsonian Centre for Astrophysics. The Solar Section of American Association of Variable Star Observers (AAVSO) is an organization that counts the number of the sunspots. The effects of interplanetary shock waves on some physical parameters can be computed using a hydrodynamical model. There should be some correlations between these effects and the sunspot variations. The major objective of this paper is twofold. The first one is to search these correlations with sunspots given in the database of AAVSO. As expected, high correlations between physical parameters and sunspots have been obtained and these are presented in tables below. The second objective is to make an estimation of these parameters for the 22nd solar cycle and the years between 2015 and 2018 using an artificial neural network. Predictions have been made for these years where no shock data is present using artificial intelligence. The correlations were observed to increase further when these prediction results were included. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.