Skip to main content
SpringerLink
Log in

Estimating the Effect of TEC Data on Rain with Modelling and Wavelet Transformation Analysis

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14104))

Included in the following conference series:

  • 501 Accesses

Abstract

Events such as geomagnetic storms and solar winds cause significant changes and deterioration in the upper layers of the earth’s atmosphere. These distortions have a significant effect on the waves and signals used by the satellites and radios we use. Based on different satellite observations, studies on geomagnetic storms, ionosphere TEC (Total Electron Content) samples, and GPS observations, we can obtain some measurements. Another important natural event in measuring temperature data and investigating its effects is rain. In this study, it is aimed to determine the effect of TEC change in the ionosphere on the daily precipitation for Izmir, Trabzon and Istanbul provinces of Turkey and to determine whether the success of the algorithms used for different regions will change according to the region, that is, the data. The temporal variation of TEC data for the period January 1–December 31, 2017 has been examined and modelled. In this study 60% of the one-year daily data was used for training, 20% for evaluation, and 20% for testing. In the first part of the study, wavelet transforms and large, medium, and small scale changes in TEC and precipitation data were examined. In the second part of the study, TEC data are modelled with artificial neural networks, support vector machines, and decision tree-based estimation methods. And in the last stage, machine learning algorithms and wavelet were used together to predict TEC data, and the prediction success ratio was tried to be related. There is a sufficient evidence of observed and estimated TEC data with alpha = 0, 05 significant level.

These authors Contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Asaly, S., Gottlieb, L.A., Reuveni, Y.: Using support vector machine (SVM) and ionospheric total electron content (TEC) data for solar flare predictions. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 1469–1481 (2020)

    Article  Google Scholar 

  2. El-Diasty, M.: Regional ionospheric modeling using wavelet network model. The J. Global Positioning Syst. 15(1), 2 (2017)

    Article  Google Scholar 

  3. Gogou, G., Maglaveras, N., Ambrosiadou, B.V., Goulis, D., Pappas, C.: A neural network approach in diabetes management by insulin administration. J. Med. Syst. 25, 119–131 (2001)

    Article  Google Scholar 

  4. Inyurt, S., Hasanpour Kashani, M., Sekertekin, A.: Ionospheric TEC forecasting using Gaussian process regression (GPR) and multiple linear regression (MLR) in Turkey. Astrophys. Space Sci. 365, 1–17 (2020)

    Article  Google Scholar 

  5. Iyer, S., Mahajan, A.: Predicting the ionospheric total electron content using adaptive regression model. In: 2021 6th International Conference for Convergence in Technology (I2CT), pp. 1–11. IEEE (2021)

    Google Scholar 

  6. Karamizadeh, S., Abdullah, S.M., Halimi, M., Shayan, J., Javad Rajabi, M.: Advantage and drawback of support vector machine functionality. In: 2014 international conference on computer, communications, and control technology (I4CT), pp. 63–65. IEEE (2014)

    Google Scholar 

  7. Nishioka, M., Saito, S., Tao, C., Shiota, D., Tsugawa, T., Ishii, M.: Statistical analysis of ionospheric total electron content (TEC): long-term estimation of extreme TEC in Japan. Earth, Planets and Space 73(1), 1–12 (2021)

    Article  Google Scholar 

  8. Şentürk, E.: Küresel İyonosfer Haritalarının Türkiye’deki Performansının GNSS Verileriyle İstatistiksel Olarak İncelenmesi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 23(67), 247–255 (2021)

    Google Scholar 

  9. Uğuz, S.: Makine öğrenmesi teorik yönleri ve Python uygulamaları ile bir yapay zekâ ekolü. Nobel Yayıncılık. Ankara (2019)

    Google Scholar 

  10. Sorkhabi, O.M.: Deep learning of total electron content. SN Appl. Sci. 3(7), 1–9 (2021). https://doi.org/10.1007/s42452-021-04674-6

    Article  Google Scholar 

  11. Tang, J., Li, Y., Yang, D., Ding, M.: An approach for predicting global ionospheric TEC using machine learning. Remote Sens. 14(7), 1585 (2022)

    Article  Google Scholar 

  12. Lei, D., et al.: Ionospheric TEC prediction base on attentional BiGRU. Atmosphere 13(7), 1039 (2022)

    Article  Google Scholar 

  13. Benoit, A.G.M.D.S., Petry, A.: Evaluation of F10.7, sunspot number and photon flux data for ionosphere TEC modeling and prediction using machine learning techniques. Atmosphere 12(9), 1202 (2021)

    Article  Google Scholar 

  14. Momin, M., Alam, M.M., Mahfuz, M.H., Islam, M.R., Habaebi, M.H., Badron, K.: Prediction of rain attenuation on earth-to-satellite link using rain rate measurement with various integration times. In: 2021 8th International Conference on Computer and Communication Engineering (ICCCE), pp. 385–390. IEEE (2021)

    Google Scholar 

  15. Ahuna, M.N., Afullo, T.J., Alonge, A.A.: Rain attenuation prediction using artificial neural network for dynamic rain fade mitigation. SAIEE Africa Res. J. 110(1), 11–18 (2019)

    Article  Google Scholar 

  16. Sodunke, M.A., Ojo, J.S., Adedayo, K.D., De, A., Sulaimon, M.O.: Prediction and analysis of seasonal rain attenuation in the South-western region of Nigeria for future microwave applications. Adv. Space Res. (2022)

    Google Scholar 

  17. Nandi, D.D., Pérez-Fontán, F., Pastoriza-Santos, V., Machado, F.: Application of synthetic storm technique for rain attenuation prediction at Ka and Q band for a temperate Location, Vigo, Spain. Adv. Space Res. 66(4), 800–880 (2020)

    Article  Google Scholar 

  18. Metlek, S., Kayaalp, K.: Makine Öğrenmesinde, Teoriden Örnek MATLAB Uygulamalarına Kadar Destek Vektör Makineleri. İksad Yayınevi (2020)

    Google Scholar 

  19. Palmer, J.M., Theisen, J.M., Duran, R.M., Grayburn, W.S., Calvo, A.M., Keller, N.P.: Secondary metabolism and development is mediated by LlmF control of VeA subcellular localization in Aspergillus nidulans. PLoS Genet. 9(1), e1003193 (2013)

    Article  Google Scholar 

  20. Kalafatoğlu, E., Ceren, E., Kaymaz, Z.: Magnetic and electric field variations during geomagnetically active days over Turkey. Adv. Space Res. 60–9, 1921–1948 (2017). https://doi.org/10.1016/j.asr.2017.07.019

    Article  Google Scholar 

  21. SVM Algorithm: https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm. Erişim Tarihi: 12 Mayıs 2023, Erişim Saati: 13:25

Download references

Author information

Authors and Affiliations

  1. Institute of Graduate Study, Istanbul Aydın University, 34295, Istanbul, Turkey

    Selcuk Doven, Büşranur Güdar & Khaled Al-Nimer

  2. Faculty of Engineering, Department of Computer Engineering, Istanbul Aydın University, 34295, Istanbul, Turkey

    Zafer Aslan

Authors
  1. Selcuk Doven
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Büşranur Güdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khaled Al-Nimer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zafer Aslan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Selcuk Doven .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Doven, S., Güdar, B., Al-Nimer, K., Aslan, Z. (2023). Estimating the Effect of TEC Data on Rain with Modelling and Wavelet Transformation Analysis. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14104. Springer, Cham. https://doi.org/10.1007/978-3-031-37105-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37105-9_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37104-2

  • Online ISBN: 978-3-031-37105-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation