Deep Neural Networks with Convolutional and LSTM layers for SYM-H and ASY-H forecasting
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/48669DOI: 10.1029/2021SW002748
ISSN: 1542-7390
Publisher
AGU
Date
2021-04-30Affiliation
Universidad de Alcalá. Departamento de Automática; Universidad de Alcalá. Departamento de Física y MatemáticasFunders
Junta de Comunidades de Castilla-La Mancha
Ministerio de Ciencia e Innovación
Ministerio de Economía y Competitividad
Bibliographic citation
Collado Villaverde, A., Muñoz, P. & Cid, C. 2021, "Deep Neural Networks with Convolutional and LSTM layers for SYM-H and ASY-H forecasting", Space Weather, vol. 19, no. 6.
Keywords
Energy management
Heating
Ventilation and air conditioning systems
Autonomic computing, Machine learning
Multi-objective optimization
Smart building
Project
info:eu-repo/grantAgreement/JCCM//SBPLY%2F19%2F180501%2F000024
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109891RB-I00/ES/MEJORA DE LA GESTION DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCION DE LA DEMANDA CON APRENDIZAJE AUTOMATICO Y PLANIFICACION/
info:eu-repo/grantAgreement/MINECO//AYA2016-80881-P
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.1029/2021SW002748Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Access rights
info:eu-repo/semantics/openAccess
Abstract
Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at midlatitude with a 1-min resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM-H and ASY-H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013-2020.
Files in this item
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Deep_Collado_Space_Weather_2021.pdf | 1.341Mb |
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Deep_Collado_Space_Weather_2021.pdf | 1.341Mb |
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