RT Journal Article
T1 A Machine Learning hourly analysis on the relation the Ionosphere and Schumann Resonance Frequency
A1 Cano-Domingo, Carlos
A1 Stoean, Ruxandra
A1 Joya-Caparrós, Gonzalo
A1 Novas-Castellano, Nuria
A1 Fernández-Ros, Manuel
A1 Gázquez-Parra, José A.
K1 Resonancia
AB The Schumann Resonances arise from the constructive interference of dozens of near-simultaneous lightning strikes every second, mostly located in the tropics. Characterizing the Schumann Resonance signal variation is a complex task due to the number of variables affecting the electromagnetic composition of the ionosphere and the Earth. We describe a novel approach for investigating the behavior of this variation by focusing on specific hours of the day. This study further explores this preliminary influence by means of a machine learning framework composed of six conceptually different algorithms. Fourteen external variables, related to the ionosphere condition, are considered as the predictors for the monthly Schumann Resonance frequency variation along five years of real data, for each of the first six modes and separated by the hour of the day. The results provide a clear evidence of the importance of selecting a particular hour to observe the influence of the Ionosphere parameters on the Schumann Resonance frequency variation.
PB Elsevier
YR 2023
FD 2023-02
LK https://hdl.handle.net/10630/41391
UL https://hdl.handle.net/10630/41391
LA eng
NO Carlos Cano-Domingo, Ruxandra Stoean, Gonzalo Joya, Nuria Novas, Manuel Fernandez-Ros, Jose Antonio Gazquez, A Machine Learning hourly analysis on the relation the Ionosphere and Schumann Resonance Frequency, Measurement, Volume 208, 2023, 112426, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2022.112426. (https://www.sciencedirect.com/science/article/pii/S0263224122016232)
NO Andalusian Institute of Geophysics. The Ministry of Economics and Competitiveness of Spain
NO Innovation, Science and Enterprise, Andalusian Regional Government
NO European Union FEDER Program and CIAMBITAL Group
NO University of Almeria
NO European Regional Development Fund (FEDER).
NO Romanian Ministry of Research and Innovation
NO Electronics Technology Department of the University of Malaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026