TY - GEN
T1 - Fuzzy Model with Meteorological Variables for the Determination of the THSW Index and the Electric Field in the Area of East Lima, Peru
AU - Soria, Juan J.
AU - Poma, Orlando
AU - Sumire, David A.
AU - Rojas, Joel Hugo Fernandez
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Electric field
KW - Fuzzy model
KW - Mandani method
KW - Meteorological variables
KW - THSW index
UR - http://www.scopus.com/inward/record.url?scp=85115873377&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-77445-5_49
DO - 10.1007/978-3-030-77445-5_49
M3 - Conference contribution
AN - SCOPUS:85115873377
SN - 9783030774448
T3 - Lecture Notes in Networks and Systems
SP - 527
EP - 544
BT - Artificial Intelligence in Intelligent Systems - Proceedings of 10th Computer Science On-line Conference, 2021
A2 - Silhavy, Radek
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th Computer Science Online Conference, CSOC 2021
Y2 - 1 April 2021 through 1 April 2021
ER -