Classification of regional ionospheric disturbance based on machine learning techniques

Date
2016
Advisor
Instructor
Source Title
European Space Agency, Special Publication
Print ISSN
0379-6566
Electronic ISSN
Publisher
European Space Agency
Volume
740
Issue
Pages
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
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.

Course
Other identifiers
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
Citation
Published Version (Please cite this version)