Predicting sunspot number from topological features in spectral images I: Machine learning approach

This study presents an advanced machine learning approach to predict the number of sunspots using a comprehensive dataset derived from solar images provided by the Solar and Heliospheric Observatory (SOHO). The dataset encompasses various spectral bands, capturing the complex dynamics of solar activ...

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Autores:: Sierra Porta, David
Tarazona Alvarado, Miguel
Herrera Acevedo, Daniel

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

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dc.title.spa.fl_str_mv	Predicting sunspot number from topological features in spectral images I: Machine learning approach
title	Predicting sunspot number from topological features in spectral images I: Machine learning approach
spellingShingle	Predicting sunspot number from topological features in spectral images I: Machine learning approach Machine learning Sunspots prediction Spectral images Sun’s dynamics Fractal features LEMB
title_short	Predicting sunspot number from topological features in spectral images I: Machine learning approach
title_full	Predicting sunspot number from topological features in spectral images I: Machine learning approach
title_fullStr	Predicting sunspot number from topological features in spectral images I: Machine learning approach
title_full_unstemmed	Predicting sunspot number from topological features in spectral images I: Machine learning approach
title_sort	Predicting sunspot number from topological features in spectral images I: Machine learning approach
dc.creator.fl_str_mv	Sierra Porta, David Tarazona Alvarado, Miguel Herrera Acevedo, Daniel
dc.contributor.author.none.fl_str_mv	Sierra Porta, David Tarazona Alvarado, Miguel Herrera Acevedo, Daniel
dc.subject.keywords.spa.fl_str_mv	Machine learning Sunspots prediction Spectral images Sun’s dynamics Fractal features
topic	Machine learning Sunspots prediction Spectral images Sun’s dynamics Fractal features LEMB
dc.subject.armarc.none.fl_str_mv	LEMB
description	This study presents an advanced machine learning approach to predict the number of sunspots using a comprehensive dataset derived from solar images provided by the Solar and Heliospheric Observatory (SOHO). The dataset encompasses various spectral bands, capturing the complex dynamics of solar activity and facilitating interdisciplinary analyses with other solar phenomena. We employed five machine learning models: Random Forest Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Ada Boost Regressor, and Hist Gradient Boosting Regressor, to predict sunspot numbers. These models utilized four key heliospheric variables — Proton Density, Temperature, Bulk Flow Speed and Interplanetary Magnetic Field (IMF) — alongside 14 newly introduced topological variables. These topological features were extracted from solar images using different filters, including HMIIGR, HMIMAG, EIT171, EIT195, EIT284, and EIT304. In total, 60 models were constructed, both incorporating and excluding the topological variables. Our analysis reveals that models incorporating the topological variables achieved significantly higher accuracy, with the r2-score improving from approximately 0.30 to 0.93 on average. The Extra Trees Regressor (ET) emerged as the best-performing model, demonstrating superior predictive capabilities across all datasets. These results underscore the potential of combining machine learning models with additional topological features from spectral analysis, offering deeper insights into the complex dynamics of solar activity and enhancing the precision of sunspot number predictions. This approach provides a novel methodology for improving space weather forecasting and contributes to a more comprehensive understanding of solar-terrestrial interactions.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-29T16:52:06Z
dc.date.available.none.fl_str_mv	2024-07-29T16:52:06Z
dc.date.issued.none.fl_str_mv	2024-07-19
dc.date.submitted.none.fl_str_mv	2024-07-29
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dc.identifier.citation.spa.fl_str_mv	Sierra-Porta, D., Tarazona-Alvarado, M., & Acevedo, D. H. (2024). Predicting sunspot number from topological features in spectral images I: Machine learning approach. Astronomy and Computing, 100857. https://doi.org/10.1016/j.ascom.2024.100857
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12585/12701
dc.identifier.doi.none.fl_str_mv	10.1016/j.ascom.2024.100857
dc.identifier.instname.spa.fl_str_mv	Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv	Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv	Sierra-Porta, D., Tarazona-Alvarado, M., & Acevedo, D. H. (2024). Predicting sunspot number from topological features in spectral images I: Machine learning approach. Astronomy and Computing, 100857. https://doi.org/10.1016/j.ascom.2024.100857 10.1016/j.ascom.2024.100857 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar
url	https://hdl.handle.net/20.500.12585/12701
dc.language.iso.spa.fl_str_mv	eng
language	eng
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eu_rights_str_mv	openAccess
dc.format.extent.none.fl_str_mv	10 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.place.spa.fl_str_mv	Cartagena de Indias
dc.source.spa.fl_str_mv	Astronomy and Computing
institution	Universidad Tecnológica de Bolívar
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spelling	Sierra Porta, David88a81b30-0b54-4821-b432-affefb13412bTarazona Alvarado, Miguelef024c8f-0c62-47e6-90e1-9de2e2566b23Herrera Acevedo, Daniel2f2e14ba-6e9b-4697-a7f7-312414a61c762024-07-29T16:52:06Z2024-07-29T16:52:06Z2024-07-192024-07-29Sierra-Porta, D., Tarazona-Alvarado, M., & Acevedo, D. H. (2024). Predicting sunspot number from topological features in spectral images I: Machine learning approach. Astronomy and Computing, 100857. https://doi.org/10.1016/j.ascom.2024.100857https://hdl.handle.net/20.500.12585/1270110.1016/j.ascom.2024.100857Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis study presents an advanced machine learning approach to predict the number of sunspots using a comprehensive dataset derived from solar images provided by the Solar and Heliospheric Observatory (SOHO). The dataset encompasses various spectral bands, capturing the complex dynamics of solar activity and facilitating interdisciplinary analyses with other solar phenomena. We employed five machine learning models: Random Forest Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Ada Boost Regressor, and Hist Gradient Boosting Regressor, to predict sunspot numbers. These models utilized four key heliospheric variables — Proton Density, Temperature, Bulk Flow Speed and Interplanetary Magnetic Field (IMF) — alongside 14 newly introduced topological variables. These topological features were extracted from solar images using different filters, including HMIIGR, HMIMAG, EIT171, EIT195, EIT284, and EIT304. In total, 60 models were constructed, both incorporating and excluding the topological variables. Our analysis reveals that models incorporating the topological variables achieved significantly higher accuracy, with the r2-score improving from approximately 0.30 to 0.93 on average. The Extra Trees Regressor (ET) emerged as the best-performing model, demonstrating superior predictive capabilities across all datasets. These results underscore the potential of combining machine learning models with additional topological features from spectral analysis, offering deeper insights into the complex dynamics of solar activity and enhancing the precision of sunspot number predictions. This approach provides a novel methodology for improving space weather forecasting and contributes to a more comprehensive understanding of solar-terrestrial interactions.10 páginasapplication/pdfenghttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccessCC0 1.0 Universalhttp://purl.org/coar/access_right/c_abf2Astronomy and ComputingPredicting sunspot number from topological features in spectral images I: Machine learning approachinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Machine learningSunspots predictionSpectral imagesSun’s dynamicsFractal featuresLEMBCartagena de IndiasPúblico generalAggarwal, A., Kumar, M., 2021. Image surface texture analysis and classification using deep learning. Multimedia Tools Appl. 80 (1), 1289–1309. doi:10.1007/s11042- 020-09520-2Alexakis, P., Mavromichalaki, H., 2019. 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Predicting sunspot number from topological features in spectral images I: Machine learning approach

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