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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.