Prediction of Ionospheric TEC Using RNN During the Indonesia Earthquakes Based on GPS Data and Comparison with the IRI Model

R. Mukesh*, Sarat C. Dass, S. Kiruthiga, S. Mythili, M. Vijay, K. Likitha Shree, M. Abinesh, T. Ambika, Pooja

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Total electron content (TEC) is a significant descriptive measure for the ionosphere of the earth. Due to either the sun’s activity like solar flare or the positive hall effect caused during earthquake (EQ), the oxygen atoms of the ionosphere split into oxygen ions and electrons increasing the electron content in the ionosphere which causes a rise in the TEC value, thus causing the delay in the signals coming from the satellite to the earth. TEC is associated with the Sun’s parameter and geomagnetic indices. In this research, parameters such as planetary K and A-index (Kp and Ap), Radio flux at 10.7 cm (F10.7), Sunspot number (SSN), and IONOLAB true TEC values were collected for the BAKO IGS network station situated in Indonesia (− 6.45° N, 106.85° E) for predicting TEC variations during EQ days occurred in the years 2004 and 2012. A total of three months of TEC data from the BAKO station during the years 2004 and 2012 were used for the developed Recurrent Neural Network (RNN) model in order to predict the TEC before and after the EQ days. For the year 2004, the model has an average Root Mean Square Error (RMSE) and Correlation Coefficient (CC) of 6.79 and 0.90. Also, for the year 2012, during April it has the average RMSE and CC of 8.90 and 0.94. For the same year in August month, the model has the average RMSE and CC of 8.70 and 0.94. The performance of the model is also evaluated using linear regression scatter plot. The Pearson’s R value calculated from the scatter plot is 0.92, shows that the model has good correlation with the true TEC.

Original languageEnglish
Title of host publication4th Congress on Intelligent Systems. CIS 2023
EditorsSandeep Kumar, Balachandran K., Joong Hoon Kim, Jagdish Chand Bansal
PublisherSpringer
Pages401-415
Number of pages15
ISBN (Electronic)9789819990375
ISBN (Print)9789819990368
DOIs
Publication statusPublished - 18 Mar 2024
Event4th Congress on Intelligent Systems 2023 - Bengaluru, India
Duration: 4 Sept 20235 Sept 2023

Publication series

NameLecture Notes in Networks and Systems
Volume868
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference4th Congress on Intelligent Systems 2023
Abbreviated titleCIS 2023
Country/TerritoryIndia
CityBengaluru
Period4/09/235/09/23

Keywords

  • BAKO
  • IONOLAB
  • RNN
  • TEC

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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