%0 Journal Article
	%A Rehab Abdulmajed and  Amr Hamada and  Ahmed Elsaid and  Hisashi Hayakawa and  Ayman Mahrous
	%D 2024
	%J International Journal of Physical and Mathematical Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 209, 2024
	%T A Comparison between Artificial Neural Network Prediction Models for Coronal Hole Related High-Speed Streams
	%U https://publications.waset.org/pdf/10013630
	%V 209
	%X Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of the solar wind using mathematical models, MHD models and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulated the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar Cycles (SC) 21, 22, 23, and most of 24.
	%P 47 - 51