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Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion

Author

Listed:
  • Filip Arnaut

    (Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia)

  • Aleksandra Kolarski

    (Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia)

  • Vladimir A. Srećković

    (Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia)

Abstract

Machine learning (ML) methods are commonly applied in the fields of extraterrestrial physics, space science, and plasma physics. In a prior publication, an ML classification technique, the Random Forest (RF) algorithm, was utilized to automatically identify and categorize erroneous signals, including instrument errors, noisy signals, outlier data points, and the impact of solar flares (SFs) on the ionosphere. This data communication includes the pre-processed dataset used in the aforementioned research, along with a workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of ML and ionospheric physics science, as well as being useful to other researchers for diverse objectives.

Suggested Citation

  • Filip Arnaut & Aleksandra Kolarski & Vladimir A. Srećković, 2024. "Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion," Data, MDPI, vol. 9(1), pages 1-6, January.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:1:p:17-:d:1321905
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