Abstract
The prediction of environmental variables is important because it facilitates understanding the processes in real time. The objective of this study was to formulate a fuzzy model of four environmental input variables, namely temperature, UV radiation, humidity and wind speed, and two environmental output variables, namely THSW index and electric field. The Mandani fuzzy method was used with 80 fuzzy rules concatenated with the “and” conector defuzzifying the THSW index and the electric field with 19 654 records. The fuzzy model was based on a maximum temperature for 2019 of 29.8 °C with an average of 24.595 °C and a maximum UV radiation of 11.7 and an average of 1.9506 nm, as well as an average humidity of 6.317%, in addition to a wind speed of 12.90 m/s resulting lower in spring with an average of 1.2727 m/s. Likewise, an average THSW index of 25.169 was obtained as output, measured with four meteorological variables with a minimum of 21.055 in spring and a maximum of 25.169 in summer. In addition, an average electric field of −1.6218 kV/m was found in winter and in summer 2019, a value of 0.1682 kV/m was obtained with a minimum value of –6.6993 kV/m and a maximum value of 3.6323 kV/m.
Robustness of the Fuzzy model was determined with the Friedman test, where the THSW index had a value of 0.0014 and the electric field had a value of 0.0021, which shows a good performance of the proposed model.
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References
Antunes de Sá, A., Marshall, R., Sousa, A., Viets, A., Deierling, W.: An array of low-cost, high-speed, autonomous electric field mills for thunderstorm research. Earth Space Sci. 7(11) (2020). https://doi.org/10.1029/2020EA001309
Azad, A., et al.: Novel approaches for air temperature prediction: a comparison of four hybrid evolutionary fuzzy models. Meteorol. Appl. 27(1) (2020). https://doi.org/10.1002/met.1817
Davis: Vantage Pro2 Manuel de la console (2019a). https://www.davisnet.com/legal.
Davis, I.: Options de collecte de données (2019b). www.weatherlink.com
Elizalde, G.: julio-diciembre. 14(2), 265–292 (2009)
Esquivel Garcia, R.: Educación Superior y formación de directivos View project Evaluación del impacto de la capacitación view project (2013). https://www.researchgate.net/publication/324278460
Haroldo, P.: Haroldo Elorza Pérez-Tejada. CERGAGE Learning (ed.) Tercera (2008)
Harrison, R.G., Nicoll, K.A., Mareev, E., Slyunyaev, N., Rycroft, M.J.: Extensive layer clouds in the global electric circuit: their effects on vertical charge distribution and storage. Proc. Roy. Soc. A: Math. Phys. Eng. Sci. 476(2238), 20190758 (2020). https://doi.org/10.1098/rspa.2019.0758
Hutter, F., Kotthoff, L., Vanschoren, J.: Automated Machine Learning Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning (2019). https://doi.org/10.1007/978-3-030-05318-5. http://www.springer.com/series/15602
Marcos, J.L., Sánchez, J.L., Merino, A., Melcón, P., Mérida, G., García-Ortega, E.: Spatial and temporal variability of hail falls and estimation of maximum diameter from meteorological variables. Atmos. Res. 247 (2021). https://doi.org/10.1016/j.atmosres.2020.105142
Martinez, M.: Medición del campo eléctrico atmosférico en la ciudad de León. Establecimiento de límites para prevención ante la ocurrencia de descargas atomosféricas (2014)
Mathworks, C.: Statistics and machine learning toolboxTM user’s guide R2020a (2020). www.mathworks.com
Matute, A., Bernal Suárez, W.: Fuzzy logic techniques in control engineering. Rev. Ciencia Innov. Tecnol. (RCIYT) III, 125–134 (2017)
Mirbagheri, S.A., Mohammadi, M., Bazrehkar, S.: Assessing the weight of weather parameters on MW signal strength by descriptive data mining. SN Appl. Sci. 2(2), 1–5 (2020). https://doi.org/10.1007/s42452-020-2093-9
Navidi, W.: Estadística para ingenieros y científicos. Pablo Eduardo Roig Vázquez (ed.) Primera (2006)
Nicoll, K.A., et al.: A global atmospheric electricity monitoring network for climate and geophysical research. J. Atmos. Solar-Terrestrial Phys. 184, 18–29 (2019). https://doi.org/10.1016/j.jastp.2019.01.003
Pareja, I.V.: El método Delphi El metodo delphi (2003)
Rick, A., Bothorel, S., Bouchon-Meunier, B., Muller, S., Rifqi, M.: Fuzzy techniques in mammographic image processing. In: Kerre, E.E., Nachtegael, M. (eds.) vol. 52, pp. 308–336. Physica-Verlag HD (2000). https://doi.org/10.1007/978-3-7908-1847-5_12
Romero, Y., Diaz, C., Meldrum, I., Arias Velasquez, R., Noel, J.: Temporal and spatial analysis of traffic – related pollutant under the influence of the seasonality and meteorological variables over an urban city in Peru. Heliyon 6(6) (2020). https://doi.org/10.1016/j.heliyon.2020.e04029
Saboya, N., Loaiza, O.L., Soria, J.J., Bustamante, J.: Fuzzy logic model for the selection of applicants to university study programs according to enrollment profile. In: Antipova, T., Rocha, A. (eds.) DSIC18 2018. AISC, vol. 850, pp. 121–133. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02351-5_16
Shan, H., Liu, Y., Stefanov, T.: Ensemble of convolutional neural networks for P300 speller in brain computer interface. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 376–394. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30490-4_31
Shiri, J.: Evaluation of a neuro-fuzzy technique in estimating pan evaporation values in low-altitude locations. Meteorol. Appl. 26(2), 204–212 (2019). https://doi.org/10.1002/met.1753
Silhavy, R.: Advances in Intelligent Systems and Computing 1225. In: Janusz Kacprzyk, P. (ed.) vol. 2, Systems Research Institute, Polish Academy of Sciences, Warsaw & Advisory (2020). http://www.springer.com/series/11156
Sivanandam, S.N., Sumathi, S., Deepa, S.N.: Introduction to fuzzy logic using MATLAB. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-35781-0
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Soria, J.J., Poma, O., Sumire, D.A., Rojas, J.H.F. (2021). Fuzzy Model with Meteorological Variables for the Determination of the THSW Index and the Electric Field in the Area of East Lima, Peru. In: Silhavy, R. (eds) Artificial Intelligence in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-030-77445-5_49
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