Skip to main content
Log in

Pre-estimation of Distance-Based Lightning Using Effective Meteorological Parameters

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Lightning can be described as a high voltage electrical discharge event that occurs between the cloud and the ground. The development of a warning system that enables us to predict lightning before it occurs and to take safety precautions is essential to minimize undesired lightning-related events. This study aims to predict lightning events 1 h in advance by using meteorological data recorded by a single meteorological station. The data used in this study consist of a total of ten atmospheric features, and lightning strikes were examined in three groups by considering distances from the station as 0–2 km (data group-1, DG-1), 2–4 km (DG-2), and 4–6 km (DG-3). The sequential forward selection (SFS) method was used to determine the most effective among the ten features. The probability of lightning for each data group was then estimated with the artificial neural networks algorithm. As a result, we found that the best lightning estimation rate among the three groups was obtained at 0–2 km distance, with 90% accuracy with only four effective features determined by the SFS method. As far as the authors are aware, a detailed analysis of the data and a distance-based lightning prediction system in the literature does not exist, and therefore, the originality and success of this study are expected to contribute to the literature for future studies.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Tilev-Tanriover, Ş.; Kahraman, A.; Kadioğlu, M.; Schultz, D.: Lightning fatalities and injuries in Turkey. Nat. Hazards Earth Syst. Sci. 15(8), 1881–1888 (2015)

    Article  Google Scholar 

  2. Wu, D.; Chen, Z.: Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning. Eng. Fail. Anal. 63, 172–181 (2016)

    Article  Google Scholar 

  3. Necci, A.; Antonioni, G.; Cozzani, V.; Krausmann, E.; Borghetti, A.; Nucci, C.A.: Assessment of lightning impact frequency for process equipment. Reliab. Eng. Syst. Saf. 130, 95–105 (2014)

    Article  Google Scholar 

  4. Harrington, D.B.: Turbine generators. In: Meyers, R.A. (ed.) Encyclopedia of Physical Science and Technology, 3rd edn, pp. 193–215. Academic Press, New York (2003). ISBN 9780122274107

  5. Habash, W.; Groza, V.; McNeill, T.; Roberts, I.: Lightning risk analysis of a power microgrid. Br. J. Adv. Sci. Technol. 3(1), 107–122 (2013)

    Google Scholar 

  6. Liu, C.-H.; Muna, Y.; Chen, Y.-T.; Kuo, C.-C.; Chang, H.-Y.: Risk analysis of lightning and surge protection devices for power energy structures. Energies 11(8), 1999 (2018)

    Article  Google Scholar 

  7. Srivastava, A.; Mishra, M.: Positioning of lightning rods using Monte Carlo technique. J. Electrostat. 76, 201–207 (2015)

    Article  Google Scholar 

  8. Price, C.; Rind, D.: A simple lightning parameterization for calculating global lightning distributions. J. Geophys. Res. Atmos. 97(D9), 9919–9933 (1992)

    Article  Google Scholar 

  9. Price, C.; Rind, D.: What determines the cloud-to-ground lightning fraction in thunderstorms? Geophys. Res. Lett. 20(6), 463–466 (1993)

    Article  Google Scholar 

  10. Giannaros, T.M.; Kotroni, V.; Lagouvardos, K.: Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model. Atmos. Res. 156, 1–13 (2015)

    Article  Google Scholar 

  11. Giannaros, T.; Lagouvardos, K.; Kotroni, V.: Performance evaluation of an operational lightning forecasting system in Europe. Nat. Hazards 85(1), 1–18 (2017)

    Article  Google Scholar 

  12. Wang, Y.; Yang, Y.; Jin, S.: Evaluation of lightning forecasting based on one lightning parameterization scheme and two diagnostic methods. Atmosphere 9(3), 99 (2018)

    Article  Google Scholar 

  13. Simon, T.; Mayr, G.J.; Umlauf, N.; Zeileis, A.: NWP-based lightning prediction using flexible count data regression. Adv. Stat. Climatol. Meteorol. Oceanogr. 5(1), 1–16 (2019)

    Article  Google Scholar 

  14. Srivastava, A.; Mishra, M.; Kumar, M.: Lightning alarm system using stochastic modelling. Nat. Hazards 75(1), 1–11 (2015)

    Article  Google Scholar 

  15. Anad, M.; Dash, A.; Kumar, M.J.; Kesarkar, A.: Prediction and classification of thunderstorms using artificial neural network. Int. J. Eng. Sci. Technol. 3(5), 4031–4035 (2011)

    Google Scholar 

  16. Johari, D.; Rahman, T.K.A.; Musirin, I.: Artificial neural network based technique for lightning prediction. In: 2007 5th Student Conference on Research and Development, 2007. IEEE, pp. 1–5 (2007)

  17. Abdullah, N.H.; Adnan, R.; Samad, A.M.; Ruslan, F.A.: Lightning forecasting modelling using artificial neural network (ANN): case study Sultan Abdul Aziz Shah Airport or Skypark Subang. In: 2018 IEEE Conference on Systems, Process and Control (ICSPC), 2018. IEEE, pp. 1–4 (2018)

  18. Ramzi, M.M.; Adnan, R.; Samad, A.M.; Ruslan, F.A.: Lightning prediction modelling using MLPNN structure. Case Study: Kuala Lumpur International Airport (KLIA). In: 2018 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 2018. IEEE, pp. 63–66 (2018)

  19. Weng, L.Y.; Omar, J.B.; Siah, Y.K.; Ahmed, S.K.; Abidin, I.B.Z.; Abdullah, N.: Lightning forecasting using ann-BP and radiosonde. In: 2010 International Conference on Intelligent Computing and Cognitive Informatics, 2010. IEEE, pp. 152–155 (2010)

  20. Bates, B.C.; Dowdy, A.J.; Chandler, R.E.: Lightning prediction for Australia using multivariate analyses of large-scale atmospheric variables. J. Appl. Meteorol. Climatol. 57(3), 525–534 (2018)

    Article  Google Scholar 

  21. Alves, E.R.; Tavares da Costa Jr., C.; Lopes, M.N.G.; da Rocha, B.R.P.; de Sá, J.A.S.: Lightning prediction using satellite atmospheric sounding data and feed-forward artificial neural network. J. Intell. Fuzzy Syst. 33(1), 79–92 (2017)

    Article  Google Scholar 

  22. Wang, G.; Kim, W.-H.; Kil, G.-S.; Park, D.-W.; Kim, S.-W.: An intelligent lightning warning system based on electromagnetic field and neural network. Energies 12(7), 1275 (2019)

    Article  Google Scholar 

  23. Odam, G.; Consultancy, G.; Barmouth, G.U.: Effects of Lightning on Assets, Facilities and Structures. Lightning Safety Institute, Louisville (2000)

    Google Scholar 

  24. Whitney, A.W.: A direct method of nonparametric measurement selection. IEEE Trans. Comput. 100(9), 1100–1103 (1971)

    Article  Google Scholar 

  25. Pratama, S.F.; Muda, A.K.; Choo, Y.-H.; Muda, N.A.: Computationally inexpensive sequential forward floating selection for acquiring significant features for authorship invarianceness in writer identification. Int. J. New Comput. Archit. Appl. 1(3), 581–598 (2011)

    Google Scholar 

  26. Yücelbaş, Ş.; Yücelbaş, C.; Tezel, G.; Özşen, S.; Yosunkaya, Ş.: Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal. Expert Syst. Appl. 102, 193–206 (2018)

    Article  Google Scholar 

  27. Cooper, M.A.: A fifth mechanism of lightning injury. Acad. Emerg. Med. 9(2), 172–174 (2002)

    Article  Google Scholar 

  28. DiMaio, V.J.; DiMaio, D.: Forensic Pathology. CRC Press, Boca Raton (2001)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Turkish State Meteorological Service for providing the data needed along this research.

Funding

No funding was used for this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Şule Yücelbaş.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yücelbaş, Ş., Erduman, A., Yücelbaş, C. et al. Pre-estimation of Distance-Based Lightning Using Effective Meteorological Parameters. Arab J Sci Eng 46, 1529–1539 (2021). https://doi.org/10.1007/s13369-020-05257-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-05257-0

Keywords

Navigation