Classification of Ionospheric Disturbances Using Long Short Term Memory Algorithm

Gul, S.E.Karatay, S.Arikan, F.2024-12-072024-12-072024.01.012165-0608https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=dspace_ku&SrcAuth=WosAPI&KeyUT=WOS:001297894700072&DestLinkType=FullRecord&DestApp=WOS_CPLhttps://hdl.handle.net/20.500.12597/33846In this study, disturbances in the ionosphere during periods of geomagnetic activity and seismic activity are classified with the Long Short Term Memory algorithm, one of the Deep Learning algorithms. It is observed that the classification Accuracy is at least 84% in the classification of five earthquake and five disturbance days based on the Total Electron Content data input.trinfo:eu-repo/semantics/openAccessDeep LearningLong Short Term Memoryionospheric disturbancesTotal Electron ContentClassification of Ionospheric Disturbances Using Long Short Term Memory AlgorithmOther10.1109/SIU61531.2024.10600803001297894700072