Abstract
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.
Keywords
- Machine learning
- Electric field
- Weather variables
- Forecast
- Regression learner app
- Accuracy
- Algorithms
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References
Bennett AJ, Harrison RG (2007) Historical background 62(10)
Boltek N (2014) EFM-100 atmospheric electric field monitor guide. www.boltek.com
Davis (2019) Vantage Pro2 Manuel de la console. https://www.davisnet.com/legal
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
Harrison RG, Nicoll KA (2018) Fair weather criteria for atmospheric electricity measurements. J Atmos Solar Terr Phys 179:239–250. https://doi.org/10.1016/j.jastp.2018.07.008
Harrison RG, Marlton GJ (2020) Fair weather electric field meter for atmospheric science platforms. J Electrostat 107. https://doi.org/10.1016/j.elstat.2020.103489
Hays PB, Roble RG (1979) A quasi-static model of global atmospheric electricity, 1. The lower atmosphere. J Geophys Res 84(A7):3291. https://doi.org/10.1029/ja084ia07p03291
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. https://doi.org/10.1016/j.jastp.2017.08.027
Mathworks C (2019a) Mastering machine learning a step-by-step guide with MATLAB
Mathworks C (2019b) Mastering machine learning a step-by-step guide with MATLAB. https://www.mathworks.com/content/dam/mathworks/ebook/gated/machine-learning-workflow-ebook.pdf
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. https://doi.org/10.1016/j.jastp.2019.01.003
Paluszek M, Thomas S (2017) MATLAB machine learning. In: MATLAB machine learning. Apress. https://doi.org/10.1007/978-1-4842-2250-8
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. https://doi.org/10.1016/j.envdev.2017.11.003
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. https://doi.org/10.1007/978-3-030-02351-5_16
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. https://doi.org/10.1007/978-3-030-51971-1_33
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. https://doi.org/10.1016/j.envsci.2019.06.004
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. https://doi.org/10.1016/j.jastp.2014.09.001
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. https://doi.org/10.1016/j.aeaoa.2019.100043
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. https://doi.org/10.1016/j.asr.2007.01.071
Williams E, Mareev E (2014) Recent progress on the global electrical circuit. Atmospheric Res 135–136:208–227. https://doi.org/10.1016/j.atmosres.2013.05.015
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. https://doi.org/10.1109/T-DEI.2008.4446764
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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. https://doi.org/10.1007/978-981-16-2380-6_17
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