Skip to main content

Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru

  • Conference paper
  • First Online:
Proceedings of Sixth International Congress on Information and Communication Technology


Environmental pollution and its effects on global warming and climate change are a key concern for all life on our planet. That is why meteorological variables such as maximum temperature, solar radiation, and ultraviolet levels were analyzed in this study, with a sample of 19564 readings. The data was collected using the Vantage Pro2 weather station, which was synchronized with the time and dates of the electric field measurements made by an EFM-100 sensor. The Machine Learning analysis was applied with the Regression Learner App, from which the linear regression model, regression tree, support vector machine, Gaussian process regression, and ensembles of tree algorithms were trained. The most optimal model for the prediction of the maximum temperature associated with the electric field was the Gaussian Process Regression with an RMSE of 1.3436. Likewise, for the meteorological variable of solar radiation, the optimal model was Regression Tree Medium with an RMSE of 1.3820 and for the meteorological variable of UV level, the most optimal model was Gaussian Process Regression (Rational quadratic) with an RMSE of 1.3410. Gaussian Process Regression allowed for the estimation and prediction of the meteorological variables and it was found that in the winter season at low temperatures the negative electric field is associated with high variability in its behavior; while at high temperatures they are associated with positive electric fields with low variability.

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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

Similar content being viewed by others


  1. Bennett AJ, Harrison RG (2007) Historical background 62(10)

    Google Scholar 

  2. Boltek N (2014) EFM-100 atmospheric electric field monitor guide.

  3. Davis (2019) Vantage Pro2 Manuel de la console.

  4. Eberhard J, Geissbuhler V (2000) Konservative und operative therapie bei harninkontinenz, deszensus und urogenital-beschwerden. Journal fur Urologie und Urogynakologie 7(5):32–46. MIT Press

    Google Scholar 

  5. Harrison RG, Nicoll KA (2018) Fair weather criteria for atmospheric electricity measurements. J Atmos Solar Terr Phys 179:239–250.

    Article  Google Scholar 

  6. Harrison RG, Marlton GJ (2020) Fair weather electric field meter for atmospheric science platforms. J Electrostat 107.

  7. Hays PB, Roble RG (1979) A quasi-static model of global atmospheric electricity, 1. The lower atmosphere. J Geophys Res 84(A7):3291.

  8. Lam MM, Freeman MP, Chisham G (2018) IMF-driven change to the Antarctic tropospheric temperature due to the global atmospheric electric circuit. J Atmos Solar Terr Phys 180:148–152.

    Article  Google Scholar 

  9. Mathworks C (2019a) Mastering machine learning a step-by-step guide with MATLAB

    Google Scholar 

  10. Mathworks C (2019b) Mastering machine learning a step-by-step guide with MATLAB.

  11. Nicoll KA, Harrison RG, Barta V, Bor J, Brugge R, Chillingarian A, Chum J, Georgoulias AK, Guha A, Kourtidis K, Kubicki M, Mareev E, Matthews J, Mkrtchyan H, Odzimek A, Raulin JP, Robert D, Silva HG, Tacza J, … Yaniv R (2019) A global atmospheric electricity monitoring network for climate and geophysical research. J Atmospheric Solar-Terrest Phys 184:18–29.

  12. Paluszek M, Thomas S (2017) MATLAB machine learning. In: MATLAB machine learning. Apress.

  13. Rositano F, Bert FE, Piñeiro G, Ferraro DO (2018) Identifying the factors that determine ecosystem services provision in Pampean agroecosystems (Argentina) using a data-mining approach. Environ Dev 25:3–11.

    Article  Google Scholar 

  14. Saboya N, Loaiza OL, Soria JJ, Bustamante J (2019) Fuzzy logic model for the selection of applicants to university study programs according to enrollment profile. Adv Intel Syst Comput 850:121–133.

    Article  Google Scholar 

  15. Soria JJ, Sumire DA, Poma OSCE (2020) Neural network model with time series for the prediction of the electric field in the East Lima Zone, Peru, vol 2, pp 395–410.

  16. Sperotto A, Molina JL, Torresan S, Critto A, Pulido-Velazquez M, Marcomini A (2019) A Bayesian networks approach for the assessment of climate change impacts on nutrients loading. Environ Sci Policy 100:21–36.

    Article  Google Scholar 

  17. Tacza J, Raulin JP, Macotela E, Norabuena E, Fernandez G, Correia E, Rycroft MJ, Harrison RG (2014) A new South American network to study the atmospheric electric field and its variations related to geophysical phenomena. J Atmos Solar Terr Phys 120:70–79.

    Article  Google Scholar 

  18. Takashima H, Hara K, Nishita-Hara C, Fujiyoshi Y, Shiraishi K, Hayashi M, Yoshino A, Takami A, Yamazaki A (2019) Short-term variation in atmospheric constituents associated with local front passage observed by a 3-D coherent Doppler lidar and in-situ aerosol/gas measurements. Atmospheric Environ X:3.

  19. Tinsley BA, Burns GB, Zhou L (2007) The role of the global electric circuit in solar and internal forcing of clouds and climate. Adv Space Res 40(7):1126–1139.

    Article  Google Scholar 

  20. Williams E, Mareev E (2014) Recent progress on the global electrical circuit. Atmospheric Res 135–136:208–227.

  21. Zeng R, Zhang Y, Chen W, Zhang B (2008) Measurement of electric field distribution along composite insulators by integrated optical electric field sensor. IEEE Trans Dielectr Electr Insul 15(1):302–310.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Juan J. Soria .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Soria, J.J., Poma, O., Sumire, D.A., Rojas, J.H.F., Echevarria, M.O. (2022). Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore.

Download citation

Publish with us

Policies and ethics