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Machine learning models for predicting geomagnetic storms across five solar cycles using Dst index and heliospheric variables
| dc.contributor.author | Sierra Porta, David | |
| dc.contributor.author | Petro Ramos, Jesús | |
| dc.contributor.author | Ruiz Morales, David | |
| dc.contributor.author | Herrera Acevedo, Daniel | |
| dc.contributor.author | García Teheran, Andrés | |
| dc.contributor.author | Tarazona Alvarado, José | |
| dc.coverage.spatial | Colombia | |
| dc.date.accessioned | 2024-09-06T14:43:25Z | |
| dc.date.available | 2024-09-06T14:43:25Z | |
| dc.date.issued | 2024-08-14 | |
| dc.date.submitted | 2024-09-05 | |
| dc.identifier.citation | D. Sierra-Porta, J.D. Petro-Ramos, D.J. Ruiz-Morales, D.D. Herrera-Acevedo, A.F. García-Teheran, M. Tarazona Alvarado, Machine learning models for predicting geomagnetic storms across five solar cycles using Dst index and heliospheric variables, Advances in Space Research, Volume 74, Issue 8, 2024, Pages 3483-3495, ISSN 0273-1177, https://doi.org/10.1016/j.asr.2024.08.031. (https://www.sciencedirect.com/science/article/pii/S0273117724008500) | spa |
| dc.identifier.uri | https://hdl.handle.net/20.500.12585/12719 | |
| dc.description.abstract | This study aims to improve the understanding of geomagnetic storms by utilizing machine learning models and analyzing several heliophysical variables, such as the interplanetary magnetic field, proton density, solar wind speed, and proton temperature. Rather than relying on traditional correlation-based methods, we employ advanced machine learning techniques to examine the complex relationships between these factors and geomagnetic storms. Our analysis covers a large dataset spanning six solar cycles, including the current 25th cycle, to provide comprehensive insights into the dynamics of these storms. Our study highlights the significance of the interplanetary magnetic field as a key predictor of geomagnetic storms, challenging previous beliefs that primarily focused on sunspot activity. By using high-resolution data, we uncover new patterns and provide a more detailed analysis of the factors influencing geomagnetic storms. We emphasize the importance of considering a range of heliophysical variables, such as proton temperature and flow pressure, which offer new insights into the complex dynamics driving these storm events. The application of machine learning models, particularly Random Forest and Gradient Boosting, demonstrated superior predictive accuracy compared to traditional methods. Our results reveal that the Dst-index MIN, scalar B, and alpha/proton ratio are among the most influential factors, accounting for a significant portion of the prediction model’s accuracy. These findings underscore the utility of machine learning in identifying critical drivers of geomagnetic activity and enhancing forecast precision. Additionally, our research underscores the need for comprehensive models that can accurately predict geomagnetic storms by integrating various data sources. This machine learning approach not only improves predictive accuracy but also enhances our understanding of the underlying mechanisms of space weather. The insights gained from this study have important implications for both scientific research and practical applications, such as improving early warning systems for geomagnetic storms and mitigating their potential impacts on Earth. | spa |
| dc.format.extent | 13 páginas | |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
| dc.source | Sciencedirect - Advances in Space Research, Vol. 74 N° 8 (2024) | spa |
| dc.title | Machine learning models for predicting geomagnetic storms across five solar cycles using Dst index and heliospheric variables | spa |
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| datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
| oaire.version | http://purl.org/coar/version/c_b1a7d7d4d402bcce | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.hasversion | info:eu-repo/semantics/draft | spa |
| dc.identifier.doi | 10.1016/j.asr.2024.08.031 | |
| dc.subject.keywords | Space weather | spa |
| dc.subject.keywords | Machine learning | spa |
| dc.subject.keywords | Statistical modeling | spa |
| dc.subject.keywords | Geomagnetic storms | spa |
| dc.subject.keywords | Data science | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.cc | CC0 1.0 Universal | * |
| dc.identifier.instname | Universidad Tecnológica de Bolívar | spa |
| dc.identifier.reponame | Repositorio Universidad Tecnológica de Bolívar | spa |
| dc.publisher.place | Cartagena de Indias | spa |
| dc.subject.armarc | LEMB | |
| dc.publisher.faculty | Ciencias Básicas | spa |
| dc.type.spa | http://purl.org/coar/resource_type/c_6501 | spa |
| dc.audience | Público general | spa |
| dc.publisher.sede | Campus Tecnológico | spa |
| oaire.resourcetype | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
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