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\begin{document}

\title{Forecasting of Auroral Pc5 Pulsations from Solar Wind Parameters Using
Machine Learning Approach.}



\author[1]{stephen Owino Omondi}%
\author[1]{Justice Allotey Pappoe}%
\author[1]{Sebwato Nasurudiin}%
\author[2]{Ayman Mohamed Mahrous}%
\affil[1]{Egypt Japan University of Science and Technology}%
\affil[2]{Space Weather Monitoring Center, Helwan University, Faculty of Science}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\begin{abstract}
The coupling between the magnetosphere and solar wind contributes to the
energy, momentum, and mass transfer between the systems. However,
geomagnetic pulsations facilitate the continuation of this process in
the magnetosphere and the production of discrete auroral arcs.
Therefore, remote-sensing the magnetospheric conditions. Data analytics
with machine learning (ML) gives insight into scalability, adaptability,
and feature extraction compared to traditional empirical models. The
availability of big data in the Svalbard network spanning 25years from
1996 motivated the current study. Hence, we present the forecasting of
auroral Pc5 pulsations from solar wind parameters using the ML
technique. In the training phase, there was a regression of 0.75 and
MSE=11.90 nT2. The relationship between Pc5 forecast and observations in
low and high geomagnetic activity and solar activity showed good
consistency with R=0.76 and MSE= 11.4 nT2. For instance, the model
adapted well to the St. Patrick geomagnetic storm of March 17th, 2015
despite uncertainties in the data. In addition, the model also adapted
well with stunning performance in all Svalbard observatories with HOP
leading with 6949 prediction events and NAL with the least. Thus, this
was consistent with previous studies in terms of Pc5 pulsations
latitudinal or L-shell dependence. Finally, validation with Kp and F10.7
indices presented excellent coherence between the models. Overall, The
ML studied the connection between solar wind and interplanetary magnetic
field properties to the ground magnetic field perturbations with good
correlation results. Hence, the model will be fit for use by the
magnetospheric community for space weather studies.%
\end{abstract}%



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