Deep learning for ionospheric TEC forecasting at mid latitude stations in Turkey

Ulukavak, Mustafa

doi:10.1007/s11600-021-00568-8

Artykuł - szczegóły

Tytuł artykułu

Deep learning for ionospheric TEC forecasting at mid latitude stations in Turkey

Autorzy

Ulukavak Mustafa

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-021-00568-8

Warianty tytułu

Języki publikacji

Abstrakty

Earth’s ionosphere is an important medium for navigation, communication, and radio wave transmission. The inadequate advances in technology do not allow enough realization of ionosphere monitoring systems globally, and most research is still limited to local research in certain parts of the world. However, new methods developed in the feld of forecasting and calculation contribute to the solution of such problems. One of the methods developed is artifcial neural networks-based deep learning method (DLM), which has become widespread in many areas recently and aimed to forecast ionospheric GPS-TEC variations with DLM. In this study, hourly resolution GPS-TEC values were obtained from fve permanent GNSS stations in Turkey. DLM model is created by using the TEC variations and 9 diferent SWC index values between the years 2016 and 2018. The forecasting process (daily, three-daily, weekly, monthly, quarterly, and semi-annual) was carried out for the prediction of the TEC variations that occurred in the frst half-year of 2019. The fndings show that the proposed deep learning-based long short-term memory architecture reveals changes in ionospheric TEC estimation under 1–5 TECU. The calculated correlation coefcient and R2 values between the forecasted GPS-TEC values and the test values are higher than 0.94.

Słowa kluczowe

ionosphere TEC deep learning space weather conditions LSTM

jonosfera TEC głęboka nauka kosmiczne warunki pogodowe LSTM

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2021

Tom

Vol. 69, no. 2

Strony

589--606

Opis fizyczny

Bibliogr. 78 poz.

Twórcy

autor

Ulukavak Mustafa

mulukavak@gmail.com

Department of Geomatics Engineering, Engineering Faculty, Harran University, Şanlıurfa, Turkey

https://orcid.org/0000-0003-2092-3075

Bibliografia

1. Abe OE, Otero Villamide X, Paparini C et al (2017) Performance evaluation of GNSS-TEC estimation techniques at the grid point in middle and low latitudes during different geomagnetic conditions. J Geod 91:409–417. https://doi.org/10.1007/s00190-016-0972-z
2. Adewale A, Oyeyemi E (2018) Estimation of GPS-TEC using different techniques and comparison with values from IRI-2012, NeQuick-2 and IRI-Plas 2015 models during geomagnetic storms. In: 42nd COSPAR Scientific Assembly. p C1.1-123-18
3. Afraimovich EL, Astafyeva EI (2008) TEC anomalies—local TEC changes prior to earthquakes or TEC response to solar and geomagnetic activity changes? Earth Planets Sp 60:961–966. https://doi.org/10.1186/BF03352851
4. Aggarwal M (2015) Anomalous changes in ionospheric TEC during an earthquake event of 13–14 April 2010 in the Chinese sector. Adv Sp Res 56:1400–1412. https://doi.org/10.1016/j.asr.2015.07.007
5. Al-Hawawreh M, Moustafa N, Sitnikova E (2018) Identification of malicious activities in industrial internet of things based on deep learning models. J Inf Secur Appl 41:1–11. https://doi.org/10.1016/j.jisa.2018.05.002
6. Almalaq A, Edwards G (2017) A review of deep learning methods applied on load forecasting. In: Proceedings—16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, pp 511–516
7. Astafyeva E, Heki K (2011) Vertical TEC over seismically active region during low solar activity. J Atmos Solar-Terrestrial Phys 73:1643–1652. https://doi.org/10.1016/j.jastp.2011.02.020
8. Atıcı R (2018) Comparison of GPS TEC with modelled values from IRI 2016 and IRI-PLAS over Istanbul. Turkey Astrophys Space Sci. https://doi.org/10.1007/s10509-018-3457-0
9. Atıcı R, Sağır S (2017) The Investigation of relationship between solar parameters and total electron content over mid-latitude ıonosphere. Celal Bayar Univ J Sci 13:707–716. https://doi.org/10.18466/cbayarfbe.339346
10. Bruevich EA, Bruevich VV, Yakunina GV (2014) Changed Relation between Solar 10.7-cm radio flux and some activity ındices which describe the radiation at different altitudes of atmosphere during cycles 21–23. J Astrophys Astron 35:1–15. https://doi.org/10.1007/s12036-014-9258-0
11. Caton RG, Carrano CS, Alcala CM et al (2009) Simulating the effects of scintillation on transionospheric signals with a two-way phase screen constructed from ALTAIR phase-derived TEC. Radio Sci 44. https://doi.org/10.1029/2008RS004047
12. Che Z, Purushotham S, Cho K et al (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8:1–12. https://doi.org/10.1038/s41598-018-24271-9
13. Chekole DA, Giday NM (2020) Evaluation of ionospheric and solar proxy indices for IRI-Plas 2017 model over the East African equatorial region during solar cycle 24. Adv Sp Res 66:604–611. https://doi.org/10.1016/j.asr.2020.04.029
14. Chen K, Zhou Y, Dai F (2015) A LSTM-based method for stock returns prediction: A case study of China stock market. In: Proceedings—2015 IEEE International Conference on Big Data, IEEE Big Data 2015, pp 2823–2824
15. Chen Z, Jin M, Deng Y et al (2019) Improvement of a deep learning algorithm for total electron content maps: ımage completion. J Geophys Res Sp Phys 124:790–800. https://doi.org/10.1029/2018JA026167
16. Chernyshov AA, Miloch WJ, Jin Y, Zakharov VI (2020) Relationship between TEC jumps and auroral substorm in the high-latitude ionosphere. Sci Rep 10:6363. https://doi.org/10.1038/s41598-020-63422-9
17. Cherrier N, Castaings T, Boulch A (2017a) Forecasting ıonospheric total electron content maps with deep neural networks. In: Big data from space (BIDS), ESA Workshop. Paris, France, pp 1–4
18. Cherrier N, Castaings T, Boulch A (2017b) Deep sequence-to-sequence neural networks for ionospheric activity map prediction. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 545–555
19. Chunli D, Jinsong P (2009) Modeling and prediction of TEC in China region for satellite navigation. In: 2009 15th Asia-Pacific Conference on Communications, APCC 2009, pp 310–313
20. Contadakis ME, Arabelos DN, Pikridas C, Spatalas SD (2012) Total electron content variations over southern Europe before and during the M 6.3 Abruzzo earthquake of April 6, 2009. Ann Geophys 55:83–93. https://doi.org/10.4401/ag-5322
21. Dach R, Hugentobler U, Fridez P, Meindl M (2007) Bernese GPS software 5.0. University of Bern, Bern
22. Dashora N, Sharma S, Dabas RS et al (2009) Large enhancements in low latitude total electron content during 15 May 2005 geomagnetic storm in Indian zone. Ann Geophys 27:1803–1820. https://doi.org/10.5194/angeo-27-1803-2009
23. Durmaz M, Karslioglu MO (2015) Regional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS). J Geod 89:347–360. https://doi.org/10.1007/s00190-014-0779-8
24. Elmunim NA, Abdullah M, Hasbi AM (2017) Improving ionospheric forecasting using statistical method for accurate GPS positioning over Malaysia. In: 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016, pp 352–355
25. Fu G, Liu C, Zhou R et al (2017) Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens 9:1–21. https://doi.org/10.3390/rs9050498
26. Gao S, Zhang Y, Jia K et al (2015) Single sample face recognition via learning deep supervised autoencoders. IEEE Trans Inf Forensics Secur 10:2108–2118. https://doi.org/10.1109/TIFS.2015.2446438
27. Gensler A, Henze J, Sick B, Raabe N (2017) Deep learning for solar power forecasting—an approach using autoencoder and LSTM neural networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016—Conference Proceedings, pp 2858–2865
28. Grover A, Kapoor A, Horvitz E (2015) A deep hybrid model for weather forecasting. In: Proceedings of the ACM SIGKDD ınternational conference on knowledge discovery and data mining, pp 379–386
29. Gruet MA, Chandorkar M, Sicard A, Camporeale E (2018) Multiple-hour-ahead forecast of the dst index using a combination of long short-term memory neural network and Gaussian process. Sp Weather 16:1882–1896. https://doi.org/10.1029/2018SW001898
30. Hada-Muranushi Y, Muranushi T, Asai A, et al (2016) A deep-learning approach for operation of an automated realtime flare forecast, pp 1–6
31. Hattori K, Kon S, Nishihashi M (2011) Ionospheric anomalies possibly associated with M ≥ 6 earthquakes in Japan during 1998–2011: case studies and statistical study. In: 2011 30th URSI General Assembly and Scientific Symposium, URSIGASS 2011
32. Hernández-Pajares M, Juan JM, Sanz J et al (2009) The IGS VTEC maps: a reliable source of ionospheric information since 1998. J Geod 83:263–275. https://doi.org/10.1007/s00190-008-0266-1
33. Hofmann-Wellenhof B, Lichtenegger H, Wasle E (2008) GNSS—Global navigation satellite systems, 1st edn. Springer-Verlag Wien, Wien
34. Huang Z, Yuan H (2014) Research on regional ionospheric TEC modeling using RBF neural network. Sci China Technol Sci 57:1198–1205. https://doi.org/10.1007/s11431-014-5550-0
35. Inyurt S (2020) Modeling and comparison of two geomagnetic storms. Adv Sp Res 65:966–977. https://doi.org/10.1016/j.asr.2019.11.004
36. Jakowski N, Hoque MM, Mayer C (2011) A new global TEC model for estimating transionospheric radio wave propagation errors. J Geod 85:965–974. https://doi.org/10.1007/s00190-011-0455-1
37. Jee G, Lee HB, Kim YH et al (2010) Assessment of GPS global ionosphere maps (GIM) by comparison between CODE GIM and TOPEX/Jason TEC data: ıonospheric perspective. J Geophys Res Sp Phys. https://doi.org/10.1029/2010JA015432
38. Jin R, Jin S, Feng G (2012) M_DCB: Matlab code for estimating GNSS satellite and receiver differential code biases. GPS Solut 16:541–548. https://doi.org/10.1007/s10291-012-0279-3
39. Jin S, Jin R, Li JH (2014) Pattern and evolution of seismo-ionospheric disturbances following the 2011 Tohoku earthquakes from GPS observations. J Geophys Res Sp Phys 119:7914–7927. https://doi.org/10.1002/2014JA019825
40. Judge DL, McMullin DR, Ogawa HS, et al (1998) First solar EUV irradiances obtained from SOHO by the CELIAS/SEM. In: Solar Physics, pp 161–173
41. Kamide Y, Yokoyama N, Gonzalez W et al (1998) Two-step development of geomagnetic storms. J Geophys Res Phys 103:6917–6921. https://doi.org/10.1029/97ja03337
42. King JH, Papitashvili NE (2005) Solar wind spatial scales in and comparisons of hourly Wind and ACE plasma and magnetic field data. J Geophys Res Sp Phys 110:1–8. https://doi.org/10.1029/2004JA010649
43. Klobuchar J, Mendillo M, Basu S, et al (1973) Total electron content studies of the ıonosphere. Massachusetts
44. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
45. Li X, Guo D (2010) Modeling and prediction of ionospheric total electron content by time series analysis. In: Proceedings—2nd IEEE International Conference on Advanced Computer Control, ICACC 2010. pp 375–379
46. Millward GH, Müller-Wodarg ICF, Aylward AD et al (2001) An investigation into the influence of tidal forcing on F region equatorial vertical ion drift using a global ionosphere-thermosphere model with coupled electrodynamics. J Geophys Res Sp Phys 106:24733–24744. https://doi.org/10.1029/2000ja000342
47. Niu R, Guo C, Zhang Y, et al (2014) Study of ionospheric TEC short-term forecast model based on combination method. In: International conference on signal processing proceedings, ICSP, pp 2426–2430
48. Pulinets SA, Legen’ka AD (2003) Spatial-temporal characteristics of large scale disturbances of electron density observed in the ionospheric F-region before strong earthquakes. Cosm Res 41:221–229. https://doi.org/10.1023/A:1024046814173
49. Ratcliffe JA, Holzer TE (1973) An ıntroduction to the ıonosphere and magnetosphere. Cambridge University Press, Cambridge
50. Reddybattula KD, Panda SK, Ansari K, Peddi VSR (2019) Analysis of ionospheric TEC from GPS, GIM and global ionosphere models during moderate, strong, and extreme geomagnetic storms over Indian region. Acta Astronaut 161:283–292. https://doi.org/10.1016/j.actaastro.2019.05.042
51. Schaer S (1999) Mapping and predicting the earth’s ionosphere using the Global Positioning System. University of Berne, Switzerland
52. Schmidt M, Bilitza D, Shum CK, Zeilhofer C (2008) Regional 4-D modeling of the ionospheric electron density. Adv Sp Res 42:782–790. https://doi.org/10.1016/j.asr.2007.02.050
53. Seemala GK (2011) GPS-TEC Analysis Application version 2.9 read me. Institute for Scientific Research, Boston College, USA. https://seemala.blogspot.com/2014/01/rinex-gps-tec-program-version-29.html?m=1. Accessed 22 July 2019
54. Senalp ET, Tulunay E, Tulunay Y (2008) Total electron content (TEC) forecasting by cascade modeling: a possible alternative to the IRI-2001. Radio Sci. https://doi.org/10.1029/2007rs003719
55. Şentürk E, Çepni MS (2019) Performance of different weighting and surface fitting techniques on station-wise TEC calculation and modified sine weighting supported by the sun effect. J Spat Sci 64:209–220
56. Shen K (2018) Effect of batch size on training dynamics. https://medium.com/mini-distill/effect-of-batch-size-on-training-dynamics-21c14f7a716e#
57. Simoncini M, Taccari L, Sambo F et al (2018) Vehicle classification from low-frequency GPS data with recurrent neural networks. Transp Res Part C Emerg Technol 91:176–191. https://doi.org/10.1016/j.trc.2018.03.024
58. Sirinukunwattana K, Raza SEA, Tsang YW et al (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology ımages. IEEE Trans Med Imag 35:1196–1206. https://doi.org/10.1109/TMI.2016.2525803
59. Srivani I, Siva Vara Prasad G, Venkata Ratnam D (2019) A deep learning-based approach to forecast ıonospheric delays for GPS signals. IEEE Geosci Remote Sens Lett 16:1180–1184. https://doi.org/10.1109/LGRS.2019.2895112
60. Sun W, Xu L, Huang X, et al (2017) Forecasting of ionospheric vertical total electron content (TEC) using LSTM networks. In: Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017
61. Sundermeyer M, Oparin I, Gauvain JL, et al (2013) Comparison of feedforward and recurrent neural network language models. In: ICASSP, IEEE ınternational conference on acoustics, speech and signal processing—proceedings, pp 8430–8434
62. Swamy KCT, Sarma AD, Srinivas VS et al (2013) Accuracy evaluation of estimated ıonospheric delay of GPS signals based on Klobuchar and IRI-2007 models in low latitude region. IEEE Geosci Remote Sens Lett 10:1557–1561. https://doi.org/10.1109/LGRS.2013.2262035
63. Tan Y, Hu Q, Wang Z, Zhong Q (2018) Geomagnetic Index Kp forecasting with LSTM. Sp Weather 16:406–416. https://doi.org/10.1002/2017SW001764
64. Tariq MA, Shah M, Inyurt S et al (2020) Comparison of TEC from IRI-2016 and GPS during the low solar activity over Turkey. Astrophys Space Sci. https://doi.org/10.1007/s10509-020-03894-3
65. Tariq MA, Shah M, Ulukavak M, Iqbal T (2019) Comparison of TEC from GPS and IRI-2016 model over different regions of Pakistan during 2015–2017. Adv Sp Res 64:707–718. https://doi.org/10.1016/j.asr.2019.05.019
66. Tsurutani BT, Judge DL, Guarnieri FL et al (2005) The October 28, 2003 extreme EUV solar flare and resultant extreme ionospheric effects: comparison to other Halloween events and the Bastille Day event. Geophys Res Lett 32:1–4. https://doi.org/10.1029/2004GL021475
67. Tulunay E, Senalp ET, Cander LR et al (2004) Development of algorithms and software for forecasting, nowcasting and variability of TEC. Ann Geophys 47:1201–1214. https://doi.org/10.4401/ag-3294
68. Tulunay E, Senalp ET, Radicella SM, Tulunay Y (2006) Forecasting total electron content maps by neural network technique. Radio Sci. https://doi.org/10.1029/2005RS003285
69. Ulukavak M, Inyurt S (2020) Detection of possible ionospheric precursor caused by Papua New Guinea earthquake (Mw 7.5). Environ Monit Assess. https://doi.org/10.1007/s10661-020-8146-0
70. Ulukavak M, Yalçınkaya M, Kayıkçı ET et al (2020) Analysis of ionospheric TEC anomalies for global earthquakes during 2000–2019 with respect to earthquake magnitude (Mw≥6.0). J Geodyn. https://doi.org/10.1016/j.jog.2020.101721
71. Vitinsky YI, Kopecky M, Kuklin GV (1986) The statistics of sunspot-formation activity. Izdatel’stvo Nauka, Moscow
72. Xu Z, Wang W, Wang B (2012) Ionosphere TEC prediction based on chaos. In: 2012 10th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2012, pp 458–460
73. Yan H, Ouyang H (2018) Financial time series prediction based on deep learning. Wirel Pers Commun 102:683–700. https://doi.org/10.1007/s11277-017-5086-2
74. Yang Y, Mao Y, Sun B (2020) Basic performance and future developments of BeiDou global navigation satellite system. Satell Navig 1:1. https://doi.org/10.1186/s43020-019-0006-0
75. Yuan T, Chen Y, Liu S, Gong J (2018) Prediction model for ionospheric total electron content based on deep learning recurrent neural network (in Chinese). Chin J Sp Sci 38:48–57. https://doi.org/10.11728/cjss2018.01.048
76. Zhang Q, Wang H, Dong J et al (2017) Prediction of sea surface temperature using long short-term memory. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/LGRS.2017.2733548
77. Zhao R, Yan R, Chen Z et al (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237
78. Zolesi B, Cander LR (2014) Ionospheric prediction and forecasting, 1st edn. Springer, Berlin

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-b854d3ea-855d-409e-82f9-47fde30038a8