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    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
    (Elsevier Sci Ltd, 2020) Cavus, Huseyin; Araz, Gokhan; Coban, Gani Caglar; Raheem, Abd-ur; Karafistan, Aysel I.
    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.
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    Predicting the physical parameters of interplanetary shock waves using Artificial Neural Networks trained on NASA's ACE and WIND spacecrafts
    (Institute of Electrical and Electronics Engineers Inc., 2020) Coban, Gani Caglar; Raheem, Abd-Ur; Cavus, Huseyin
    This study aims to develop artificial neural networks to predict the physical parameters of the shock waves in the interplanetary (IP) environment which are closely correlated to the sunspot number as demonstrated in previous studies [1] and [2]. This is done by training the ANNs with the current available data and then use the model to predict for the years where there is no data present. For this purpose, NASA's Advanced Composition Explorer (ACE) and WIND spacecrafts are used to obtain the shock data and then physical parameters are calculated using the Cavus2013 hydrodynamical model. These physical parameters describe the properties of the IP shock waves. Predictions have been made where there is no data measured by the spacecrafts. This is achievable due to the presence of high correlation between the sunspot number and the calculated physical parameters of the shock waves. The ANNs regression is very close to 1. This is also shown in the results and proved as an increase in the correlation is observed when the predicted data is added to the actual data. © 2020 IEEE.