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


Abri R., ARTUNER H., Abri S., Cetin S.

11th International Conference on Data Science, Technology and Applications (DATA), Lisbon, Portugal, 11 - 13 July 2022, pp.158-167 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.5220/0011332900003269
  • City: Lisbon
  • Country: Portugal
  • Page Numbers: pp.158-167
  • Keywords: Ionosphere, Total Electron Content, Deep Autoencoder, Deep Neural Networks, Linear Discriminant Analysis, GIM-TEC DATA, GPS-TEC, EARTHQUAKES, ANOMALIES

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.