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Detection of GNSS-TEC Noise Related to the Tonga Volcanic Eruption Using Optimization Machine Learning Techniques and Integrated Data

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Advances in Geospatial Technology in Mining and Earth Sciences (GTER 2022)

Part of the book series: Environmental Science and Engineering ((ESE))

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Abstract

Total Electron Content (TEC) is the integral of the electron density along the path between receivers and satellites. TEC measured from Global Navigation Satellite Systems (GNSS) data is valuable to monitor space weather and correct ionospheric models. TEC noise detection is also an essential channel to forecast space weather and research the relationship between the atmosphere and natural phenomena like geomagnetic storms, earthquakes, volcanos, and tsunamis. In this study, we apply optimization machine learning techniques and integrated GNSS and solar activity data to determine GNSS-TEC noise at the International GNSS Service (IGS) stations in the Tonga volcanic region. We investigate 38 indices related to the geomagnetic field and solar wind plasma to select the essential parameters for forecast models. The findings show the best-suited parameters to predict vertical TEC time series: plasma temperature (or Plasma speed), proton density, Lyman alpha, R sunspot, Ap index (or Kp, Dst), and F10.7 index. Applying the Ensemble algorithm to build the TEC forecast models at the investigated IGS stations gets the accuracy from 1.01 to 3.17 TECU. The study also shows that machine learning combined with integrated data can provide a robust approach to detecting TEC noise caused by seismic activities.

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Notes

  1. 1.

    https://directory.eoportal.org/web/eoportal.

  2. 2.

    https://cddis.nasa.gov/.

  3. 3.

    https://www.swpc.noaa.gov/.

  4. 4.

    http://wdc.kugi.kyoto-u.ac.jp/.

  5. 5.

    https://www.spaceweatherlive.com/.

  6. 6.

    https://geofon.gfz-potsdam.de/.

  7. 7.

    https://www.usgs.gov/.

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Author information

Authors and Affiliations

  1. GFZ German Research Centre for Geosciences, Potsdam, Germany

    Nhung Le, Benjamin Männel, Thai Chinh Nguyen & Harald Schuh

  2. Technische Universität Berlin, Berlin, Germany

    Nhung Le & Harald Schuh

  3. Hanoi University of Natural Resources and Environment, Hanoi, Vietnam

    Nhung Le

  4. National Center for Airborne Laser Map**, University of Houston, Houston, USA

    Luyen K. Bui

  5. Hanoi University of Mining and Geology, Hanoi, Vietnam

    Luyen K. Bui & Thai Chinh Nguyen

  6. MathWorks, Munchen, Germany

    Mihaela Jarema

Authors
  1. Nhung Le
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  2. Benjamin Männel
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  3. Luyen K. Bui
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  4. Mihaela Jarema
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  5. Thai Chinh Nguyen
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  6. Harald Schuh
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Corresponding author

Correspondence to Nhung Le .

Editor information

Editors and Affiliations

  1. Department of Mine Surveying, Hanoi University of Mining and Geology, Hanoi, Vietnam

    Long Quoc Nguyen

  2. Department of Geodesy, Hanoi University of Mining and Geology, Hanoi, Vietnam

    Luyen Khac Bui

  3. Department of Surface Mining, Hanoi University of Mining and Geology, Hanoi, Vietnam

    Xuan-Nam Bui

  4. Department of Geology, Hanoi University of Mining and Geology, Hanoi, Vietnam

    Hai Thanh Tran

Appendix

Appendix

See Tables 4 and 5.

Table 4 F-test of the feature importance to select the best-fitted predictors for the regression ML models corresponding to four IGS stations AUCK, FTNA, THTI, and WARK
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Table 5 The correlation matrix of 20 significant features at station WARK
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Le, N., Männel, B., Bui, L.K., Jarema, M., Nguyen, T.C., Schuh, H. (2023). Detection of GNSS-TEC Noise Related to the Tonga Volcanic Eruption Using Optimization Machine Learning Techniques and Integrated Data. In: Nguyen, L.Q., Bui, L.K., Bui, XN., Tran, H.T. (eds) Advances in Geospatial Technology in Mining and Earth Sciences. GTER 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-20463-0_9

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