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      • Department of Electrical and Electronics Engineering
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      Classification of regional ionospheric disturbance based on machine learning techniques

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      Author(s)
      Terzi, Merve Begüm
      Arıkan, Orhan
      Karatay, S.
      Arıkan, F.
      Gulyaeva, T.
      Date
      2016
      Source Title
      European Space Agency, Special Publication
      Print ISSN
      0379-6566
      Publisher
      European Space Agency
      Volume
      740
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      196
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      Abstract
      In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.
      Keywords
      Ionosphere
      Kernel functions
      Space weather
      Artificial intelligence
      Fighter aircraft
      Geomagnetism
      Global positioning system
      Ionospheric measurement
      Learning algorithms
      NASA
      Support vector machines
      Vector spaces
      Automated classification
      Classification technique
      Ionospheric disturbance
      Machine learning techniques
      Mid-latitude ionosphere
      Total electron content
      Learning systems
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      http://hdl.handle.net/11693/37512
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      • Department of Electrical and Electronics Engineering 3702
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