Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN)

被引:35
作者
Inyurt, Samed [1 ]
Sekertekin, Aliihsan [2 ]
机构
[1] Zonguldak Bulent Ecevit Univ, Dept Geomat Engn, Zonguldak, Turkey
[2] Cukurova Univ, Ceyhan Engn Fac, Dept Geomat Engn, Adana, Turkey
关键词
Total Electron Content (TEC); Artificial Neural Network (ANN); Ionosphere; Modeling; ELECTRON-CONTENT; TEC;
D O I
10.1007/s10509-019-3545-9
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
The aim of this study was to model and predict seasonal ionospheric total electron content (TEC) using artificial neural network (ANN). Within this scope, GPS observations acquired from ANKR GPS station (Turkey) in 2015 were utilized to model TEC variations. Considering all data for each season, training and testing data were set as 80% and 10%, respectively, and the rest of the data were used to estimate TEC values using extracted mathematical models of ANN method. Day of Year (DOY), hour, F107 cm index (solar activity), Kp index and DsT index (magnetic storm index) were considered as the input parameters in ANN. The performances of ANN models were evaluated using RMSE and R statistical metrics for each season. As a result of the analyses, considering the prediction results, ANN presented more successful predictions of TEC values in winter and autumn than summer and spring with RMSE 3.92 TECU and 3.97 TECU, respectively. On the other hand the R value of winter data set (0.74) was lower than the autumn data set (0.88) while the RMSE values were opposite. This situation can be caused by the accuracy and precision of data sets. The results showed that the ANN model predicted GPS-TEC in a good agreement for ANKR station.
引用
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页数:8
相关论文
共 26 条
[1]   Prediction of Daily Dewpoint Temperature Using a Model Combining the Support Vector Machine with Firefly Algorithm [J].
Al-Shammari, Eiman Tamah ;
Mohammadi, Kasra ;
Keivani, Afram ;
Ab Hamid, Siti Hafizah ;
Akib, Shatirah ;
Shamshirband, Shahaboddin ;
Petkovic, Dalibor .
JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, 2016, 142 (05)
[2]   An Overview of Ionosphere-Thermosphere Models Available for Space Weather Purposes [J].
Belehaki, A. ;
Stanislawska, I. ;
Lilensten, J. .
SPACE SCIENCE REVIEWS, 2009, 147 (3-4) :271-313
[3]   Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models [J].
Bilgili, Mehmet ;
Sahin, Besir ;
Sangun, Levent .
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2013, 185 (01) :347-358
[4]   Prediction of soil temperature using regression and artificial neural network models [J].
Bilgili, Mehmet .
METEOROLOGY AND ATMOSPHERIC PHYSICS, 2010, 110 (1-2) :59-70
[5]  
CANDER L, ANN GEOPHYS, V41, P757
[6]   Calibration errors on experimental slant total electron content (TEC) determined with GPS [J].
Ciraolo, L. ;
Azpilicueta, F. ;
Brunini, C. ;
Meza, A. ;
Radicella, S. M. .
JOURNAL OF GEODESY, 2007, 81 (02) :111-120
[7]   Temporal-Spatial Variation of Global GPS-Derived Total Electron Content, 1999-2013 [J].
Guo, Jinyun ;
Li, Wang ;
Liu, Xin ;
Kong, Qiaoli ;
Zhao, Chunmei ;
Guo, Bin .
PLOS ONE, 2015, 10 (07)
[8]   Prediction of global positioning system total electron content using Neural Networks over South Africa [J].
Habarulema, John Bosco ;
McKinnell, Lee-Anne ;
Cilliers, Pierre J. .
JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS, 2007, 69 (15) :1842-1850
[9]   Neural network modeling of the ionospheric electron content at global scale using GPS data [J].
HernandezPajares, M ;
Juan, JM ;
Sanz, J .
RADIO SCIENCE, 1997, 32 (03) :1081-1089
[10]   Ionospheric single-station TEC short-term forecast using RBF neural network [J].
Huang, Z. ;
Yuan, H. .
RADIO SCIENCE, 2014, 49 (04) :283-292