Classification of regional ionospheric disturbance based on machine learning techniques
Date
2016Source Title
European Space Agency, Special Publication
Print ISSN
0379-6566
Publisher
European Space Agency
Volume
740
Language
English
Type
Conference PaperItem Usage Stats
237
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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
IonosphereKernel 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
Permalink
http://hdl.handle.net/11693/37512Collections
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