2021
DOI: 10.1007/s11600-021-00568-8
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Deep learning for ionospheric TEC forecasting at mid-latitude stations in Turkey

Mustafa Ulukavak
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Cited by 18 publications
(9 citation statements)
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“…Considering the coupling effect of two solar activity parameters on TEC in different aspects, F10.7 12 and R 12 are introduced into the temporal model together as independent variables. This paper focuses on the prediction and analysis of TEC, so we assume that the prediction of solar activity parameters is known [64,65]. The time resolution of the temporal model is 1h.…”
Section: Modeling Determination 231 Modeling Of Temporal Characteristicsmentioning
confidence: 99%
“…Considering the coupling effect of two solar activity parameters on TEC in different aspects, F10.7 12 and R 12 are introduced into the temporal model together as independent variables. This paper focuses on the prediction and analysis of TEC, so we assume that the prediction of solar activity parameters is known [64,65]. The time resolution of the temporal model is 1h.…”
Section: Modeling Determination 231 Modeling Of Temporal Characteristicsmentioning
confidence: 99%
“…Another paper working on the prediction of TEC values using a network of local stations in Turkey is [7]. Unlike the present paper which deals with stations distributed all over the globe, the authors use five stations, all located in the mid-latitude region.…”
Section: Global Approachmentioning
confidence: 99%
“…In recent years, various deep learning methods have been utilized for TEC prediction (Li et al., 2022; Liu et al., 2022; Mallika et al., 2018; Ren et al., 2022; Ruwali et al., 2020; Sivakrishna et al., 2022; Tang, Li, Ding, et al., 2022; Tang, Li, Yang, & Ding, 2022; Ulukavak, 2021; Xiong et al., 2021). These studies demonstrate that deep learning methods are well‐suited for handling complex and non‐linear features in the ionosphere.…”
Section: Introductionmentioning
confidence: 99%