Terzi, Merve BegümArıkan, OrhanKaratay, S.Arıkan, F.Gulyaeva, T.2018-04-122018-04-1220160379-6566http://hdl.handle.net/11693/37512Date of Conference: 09-13 May 2016Conference Name: 4th Living Planet Symposium, LPS 2016In 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.EnglishIonosphereKernel functionsSpace weatherArtificial intelligenceFighter aircraftGeomagnetismGlobal positioning systemIonospheric measurementLearning algorithmsNASASupport vector machinesVector spacesAutomated classificationClassification techniqueIonospheric disturbanceMachine learning techniquesMid-latitude ionosphereTotal electron contentLearning systemsConference Paper