Classification
of Ionospheric Scintillations during high Solar Activity and Geomagnetic Storm over
Visakhapatnam Region using Machine Learning Approach
Shiva Kumar Nimmakayala and Srilatha Indira Dutt V.B.S.
Disaster Advances; Vol. 17(4); 11-17;
doi: https://doi.org/10.25303/174da011017; (2024)
Abstract
The ionospheric plasma disturbances typically correlate with irregularities in electron
density and ionospheric scintillations are produced in reaction to these variations
generating radio signal fluctuations. Geolocation services and space based communication
are endangered due to ionospheric scintillation which promptly produces fluctuations
in information collected by Global Navigation Satellite Systems and this is at its
strongest when the solar cycle is at its peak. Ionospheric space weather has a significant
impact on Global Navigation Satellite Systems (GNSS) and one crucial aspect used
in investigating ionospheric characteristics is total electron content (TEC). Due
to fluctuations in time and space, the TEC obtained from GNSS signals is nonlinear
and nonstationary.
In this study, machine learning approaches for Classification of the ionospheric
scintillations were used during the high solar activity and geomagnetic storm in
the month of July 2023. This approach enables the classification of ionospheric
phase scintillations using well-known classifiers: Decision Tree and Support Vector
Machine.
