Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network

Rayan Abri, Harun Artuner, Sara Abri, Salih Cetin

2022

Abstract

The ionosphere plays a critical role in the functioning of the atmosphere and the planet. Fluctuations and some anomalies in the ionosphere occur as a result of solar flares caused by coronal mass ejections, seismic motions, and geomagnetic activity. The Total electron content (TEC) of the ionosphere is the most important metric for studying its morphology. The purpose of this article is to examine the relationships that exist between earthquakes and TEC data. In order to accomplish this, we present a classification method for the ionosphere’s TEC data that is based on earthquakes. Deep autoencoder techniques are used for the feature extraction from TEC data. The features that were obtained were fed into dense neural networks, which are used to perform classification. In order to assess the suggested classification model, the results of the classification model are compared to the results of the LDA (Linear Discriminant Analysis) classifier model.The research results show that the suggested model enhances the accuracy of differentiating earthquakes by around 0.94, making it a useful tool for identifying ionospheric disturbances in terms of earthquakes.

Paper Citation

in Harvard Style

Abri R., Artuner H., Abri S. and Cetin S. (2022). Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 158-167. DOI: 10.5220/0011332900003269

in Bibtex Style

@conference{data22,
author={Rayan Abri and Harun Artuner and Sara Abri and Salih Cetin},
title={Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={158-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011332900003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network
SN - 978-989-758-583-8
AU - Abri R.
AU - Artuner H.
AU - Abri S.
AU - Cetin S.
PY - 2022
SP - 158
EP - 167
DO - 10.5220/0011332900003269