Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models DOI Creative Commons

S. Kiruthiga,

S. Mythili

Frontiers in Astronomy and Space Sciences, Journal Year: 2024, Volume and Issue: 11

Published: Sept. 3, 2024

Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes. This research investigates applying two modelling techniques, Autoregressive Moving Average (ARMA) and Cokriging (CoK) based models to forecast TEC changes linked events Indonesia. The study focuses on significant earthquakes: the December 2004 Sumatra earthquake August 2012 Sulawesi earthquake. GPS data from a BAKO station near Indonesia solar geomagnetic were utilized assess causes of variations. earthquake, registering magnitude 9.1–9.3, exhibited notable 5 days before event. Analysis revealed that weakly activities. Both ARMA CoK employed predict Earthquakes. model demonstrated maximum prediction 50.92 TECU Root Mean Square Error (RMSE) value 6.15, while predicted 50.68 an RMSE 6.14. having 6.6, anomalies 6 For both earthquakes, showed weak associations activities but stronger correlations earthquake-induced electric field considered stations. 54.43 3.05, 52.90 7.35. Evaluation metrics including RMSE, Absolute Deviation (MAD), Relative Error, Normalized (NRMSE) accuracy reliability models. results indicated captured general trend variations, nuances emerged their responses events. heightened sensitivity disturbances, evident day whereas more consistent performance across pre- post-earthquake periods.

Language: Английский

Multi-satellite based possible precursory signals detection linked to the 2024 Noto Peninsula Japan earthquake of Mw 7.5 DOI

Rasim Shahzad,

Munawar Shah,

Imtiaz Nabi

et al.

Advances in Space Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Ionospheric TEC forecast using universal kriging and recurrent neural network over low-latitude during the X class solar flares occurred in the year 2024 DOI
R. Mukesh, Sarat C. Dass,

M. Vijay

et al.

Journal of Space Safety Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0
Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models DOI Creative Commons

S. Kiruthiga,

S. Mythili

Frontiers in Astronomy and Space Sciences, Journal Year: 2024, Volume and Issue: 11

Published: Sept. 3, 2024

Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes. This research investigates applying two modelling techniques, Autoregressive Moving Average (ARMA) and Cokriging (CoK) based models to forecast TEC changes linked events Indonesia. The study focuses on significant earthquakes: the December 2004 Sumatra earthquake August 2012 Sulawesi earthquake. GPS data from a BAKO station near Indonesia solar geomagnetic were utilized assess causes of variations. earthquake, registering magnitude 9.1–9.3, exhibited notable 5 days before event. Analysis revealed that weakly activities. Both ARMA CoK employed predict Earthquakes. model demonstrated maximum prediction 50.92 TECU Root Mean Square Error (RMSE) value 6.15, while predicted 50.68 an RMSE 6.14. having 6.6, anomalies 6 For both earthquakes, showed weak associations activities but stronger correlations earthquake-induced electric field considered stations. 54.43 3.05, 52.90 7.35. Evaluation metrics including RMSE, Absolute Deviation (MAD), Relative Error, Normalized (NRMSE) accuracy reliability models. results indicated captured general trend variations, nuances emerged their responses events. heightened sensitivity disturbances, evident day whereas more consistent performance across pre- post-earthquake periods.

Language: Английский

Citations

0