Classification of regional ionospheric disturbance based on machine learning techniques

Series

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.

Source Title

European Space Agency, Special Publication

Publisher

European Space Agency

Course

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Book Title

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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Degree Name

Citation

Published Version (Please cite this version)

Language

English

Type

Conference Paper