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July 26, 2026PLoS ONE0 citationsOpen Access

Ionospheric TEC Prediction During Volcanic Eruptions Using Hybrid ML-DL Models

Ionospheric TEC anomalies analysis and prediction during six volcanic eruptions using a Hybrid ML-DL model and comparison with the AR/MLR Models

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Authors

RMR. MukeshSLS. LogeshSDSarat C. Dass

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Overview

Randomized trial assessing TEC prediction during volcanic eruptions, highlighting the superiority of Hybrid ML-DL models.

Key Points

  • This study aims to evaluate the effectiveness of a Hybrid ML-DL Model in predicting TEC during disturbances from volcanic eruptions.
  • Applied a Hybrid Machine Learning and Deep Learning model combining LightGBM and LSTM algorithms.
  • Compared model performance against individual LightGBM, LSTM, AR, and MLR models.
  • Evaluated models using four distinct performance metrics.
  • Hybrid ML-DL Model achieved an RMSE of 2.841 TECU during the Mt. Ruang eruption, outperforming other models.
  • LightGBM RMSE was 3.484 TECU, LSTM 5.084 TECU, MLR 5.345 TECU, and AR Model 6.285 TECU.
  • Hybrid ML-DL showed the best values in NRMSE (0.030), MBD (0.546 TECU), and RLE (0.069).
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Cite This Study

Mukesh et al. (2026) studied this question.

synapsesocial.com/papers/6a65a84ed3aea3239cd78bc4https://doi.org/10.1371/journal.pone.0354386
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