Relationship between Solar Flux and Sunspot Activity Using Several Regression Models

Authors

  • Ruben Cornelius Siagian Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Medan, Medan, 20221, Indonesia
  • Lulut Alfaris Department of Marine Technology, Politeknik Kelautan dan Perikanan, Pangandaran, 46396, Indonesia
  • Ghulab Nabi Ahmad Institute of Applied Sciences, Mangalayatan University, Aligarh, 202145, India
  • Nazish Laeiq Department of Computer Science, Institute of Technology and Management Aligarh, 202140, Indonesia
  • Aldi Cahya Muhammad Department of Electrical and Electronic Engineering, Islamic University of Technology, Kustia, 7003, Bangladesh
  • Ukta Indra Nyuswantoro Department of Structure Engineering, Asiatek Energi Mitratama, Jakarta, 12870, Indonesia
  • Budiman Nasution Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Medan, Medan, 20221, Indonesia

DOI:

https://doi.org/10.25077/jif.15.2.146-165.2023

Keywords:

Solar Flux, Sunspot Activity, Regression Analyis, Linear regression, SARIMA model analysis

Abstract

This study examines the correlation and prediction between sunspots and solar flux, two closely related factors associated with solar activity, covering the period from 2005 to 2022. The study utilizes a combination of linear regression analysis and the ARIMA prediction method to analyze the relationship between these factors and forecast their values. The analysis results reveal a significant positive correlation between sunspots and solar flux. Additionally, the ARIMA prediction method suggests that the SARIMA model can effectively forecast the values of both sunspots and solar flux for a 12-period timeframe. However, it is essential to note that this study solely focuses on correlation analysis and does not establish a causal relationship. Nonetheless, the findings contribute valuable insights into future variations in solar flux and sunspot numbers, thereby aiding scientists in comprehending and predicting solar activity's potential impact on Earth. The study recommends further research to explore additional factors that may influence the relationship between sunspots and solar flux, extend the research period to enhance the accuracy of solar activity predictions and investigate alternative prediction methods to improve the precision of forecasts.

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References

Ahmed, S. (2023). A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron. Sustainability, 15(4), 3017. DOI: https://doi.org/10.3390/su15043017

Ayodele, T., Ogunjuyigbe, A., Amedu, A., & Munda, J. (2019). Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms. Renewable Energy Focus, 29, 78–93. DOI: https://doi.org/10.1016/j.ref.2019.03.003

Ball, W., Haigh, J., Rozanov, E., Kuchar, A., Sukhodolov, T., Tummon, F., Shapiro, A., & Schmutz, W. (2016). High solar cycle spectral variations inconsistent with stratospheric ozone observations. Nature Geoscience, 9(3), 206–209. DOI: https://doi.org/10.1038/ngeo2640

Berrar, D. (2019). Cross-Validation. In Encyclopedia of Bioinformatics and Computational Biology; Elsevier: Amsterdam, Netherlands, 2019; pp. 542–545. DOI: https://doi.org/10.1016/B978-0-12-809633-8.20349-X

Bokde, N. D., Yaseen, Z. M., & Andersen, G. B. (2020). ForecastTB—An R package as a test-bench for time series forecasting—Application of wind speed and solar radiation modeling. Energies, 13(10), 2578. DOI: https://doi.org/10.3390/en13102578

Chatzistergos, T., Krivova, N. A., Ermolli, I., Yeo, K. L., Mandal, S., Solanki, S. K., Kopp, G., & Malherbe, J.-M. (2021). Reconstructing solar irradiance from historical Ca II K observations-I. Method and its validation. Astronomy & Astrophysics, 656, A104. DOI: https://doi.org/10.1051/0004-6361/202141516

Clette, F., Svalgaard, L., Vaquero, J. M., & Cliver, E. W. (2014). Revisiting the sunspot number: A 400-year perspective on the solar cycle. Space Science Reviews, 186, 35–103. DOI: https://doi.org/10.1007/s11214-014-0074-2

Ghimire, S., Deo, R. C., Downs, N. J., & Raj, N. (2019). Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia. Journal of Cleaner Production, 216, 288–310. DOI: https://doi.org/10.1016/j.jclepro.2019.01.158

Gosiewska, A., Kozak, A., & Biecek, P. (2021). Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering. Decision Support Systems, 150, 113556. DOI: https://doi.org/10.1016/j.dss.2021.113556

Guermoui, M., Benkaciali, S., Gairaa, K., Bouchouicha, K., Boulmaiz, T., & Boland, J. W. (2022). A novel ensemble learning approach for hourly global solar radiation forecasting. Neural Computing and Applications, 1–23. DOI: https://doi.org/10.1007/s00521-021-06421-9

Hao, J., & Ho, T. K. (2019). Machine learning made easy: A review of scikit-learn package in python programming language. Journal of Educational and Behavioral Statistics, 44(3), 348–361. DOI: https://doi.org/10.3102/1076998619832248

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data, 6(1), 1–16. DOI: https://doi.org/10.1186/s40537-019-0206-3

Hathaway, D. H., & Wilson, R. M. (2004). What the sunspot record tells us about space climate. Solar Physics, 224(1–2), 5–19. DOI: https://doi.org/10.1007/s11207-005-3996-8

Kaplan, K. (2023). The Characteristic properties of the Solar Activities during the Solar Cycle 24. DOI: https://doi.org/10.21203/rs.3.rs-2510436/v1

Khoo, K. S., Ahmad, I., Chew, K. W., Iwamoto, K., Bhatnagar, A., & Show, P. L. (2023). Enhanced microalgal lipid production for biofuel using different strategies including genetic modification of microalgae: A review. Progress in Energy and Combustion Science, 96, 101071. DOI: https://doi.org/10.1016/j.pecs.2023.101071

Liu, Y., Wu, H., Wang, J., & Long, M. (2022). Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting. Advances in Neural Information Processing Systems.

Lockwood, M., & Ball, W. T. (2020). Placing limits on long-term variations in quiet-Sun irradiance and their contribution to total solar irradiance and solar radiative forcing of climate. Proceedings of the Royal Society A, 476(2238), 20200077. DOI: https://doi.org/10.1098/rspa.2020.0077

Maldonado-Salguero, P., Bueso-Sánchez, M. C., Molina-García, Á., & Sánchez-Lozano, J. M. (2022). Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment. Renewable Energy, 200, 344–359. DOI: https://doi.org/10.1016/j.renene.2022.09.113

Mandea, M., & Chambodut, A. (2020). Geomagnetic field processes and their implications for space weather. Surveys in Geophysics, 41, 1611–1627. DOI: https://doi.org/10.1007/s10712-020-09598-1

Marov, M. Y. (2020). Radiation and space flights safety: An insight. Acta Astronautica, 176, 580–590. DOI: https://doi.org/10.1016/j.actaastro.2020.03.022

McCready, L., Pawsey, J. L., & Payne-Scott, R. (1947). Solar radiation at radio frequencies and its relation to sunspots. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 190(1022), 357–375. DOI: https://doi.org/10.1098/rspa.1947.0081

Pala, Z., & Atici, R. (2019). Forecasting sunspot time series using deep learning methods. Solar Physics, 294(5), 50. DOI: https://doi.org/10.1007/s11207-019-1434-6

Park, C.-Y., Hong, S.-H., Lim, S.-C., Song, B.-S., Park, S.-W., Huh, J.-H., & Kim, J.-C. (2020). Inverter efficiency analysis model based on solar power estimation using solar radiation. Processes, 8(10), 1225. DOI: https://doi.org/10.3390/pr8101225

Ramirez-Vergara, J., Bosman, L. B., Wollega, E., & Leon-Salas, W. D. (2022). Review of forecasting methods to support photovoltaic predictive maintenance. Cleaner Engineering and Technology, 100460. DOI: https://doi.org/10.1016/j.clet.2022.100460

Reinders, L. J. (2021). The fairy tale of nuclear fusion. Springer. DOI: https://doi.org/10.1007/978-3-030-64344-7

Seliya, N., Abdollah Zadeh, A., & Khoshgoftaar, T. M. (2021). A literature review on one-class classification and its potential applications in big data. Journal of Big Data, 8(1), 1–31. DOI: https://doi.org/10.1186/s40537-021-00514-x

Siddique, T., Mahmud, M. S., Keesee, A. M., Ngwira, C. M., & Connor, H. (2022). A survey of uncertainty quantification in machine learning for space weather prediction. Geosciences, 12(1), 27. DOI: https://doi.org/10.3390/geosciences12010027

Sun, F., Liu, M., Wang, Y., Wang, H., & Che, Y. (2020). The effects of 3D architectural patterns on the urban surface temperature at a neighborhood scale: Relative contributions and marginal effects. Journal of Cleaner Production, 258, 120706. DOI: https://doi.org/10.1016/j.jclepro.2020.120706

Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 12. DOI: https://doi.org/10.1186/s41239-021-00313-7

Thornton, P. E., Hasenauer, H., & White, M. A. (2000). Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: An application over complex terrain in Austria. Agricultural and Forest Meteorology, 104(4), 255–271. DOI: https://doi.org/10.1016/S0168-1923(00)00170-2

Wang, H., & Xu, R. (2002). Solar-terrestrial magnetic activity and space environment: Proceedings of the COSPAR Colloquium on Solar-Terrestrial Magnetic Activity and Space Environment (STMASE), held in the NAOC in Beijing, China, September 10-12, 2001 (Vol. 14). Elsevier.

Wang, Y.-M., Sheeley Jr, N., & Lean, J. (2002). Meridional flow and the solar cycle variation of the Sun’s open magnetic flux. The Astrophysical Journal, 580(2), 1188. DOI: https://doi.org/10.1086/343845

Wehrli, C., Schmutz, W., & Shapiro, A. (2013). Correlation of spectral solar irradiance with solar activity as measured by VIRGO. Astronomy & Astrophysics, 556, L3. DOI: https://doi.org/10.1051/0004-6361/201220864

Zhang, Z., Zhang, L., Xu, H., Creed, I. F., Blanco, J. A., Wei, X., Sun, G., Asbjornsen, H., & Bishop, K. (2023). Forest water-use efficiency: Effects of climate change and management on the coupling of carbon and water processes. Forest Ecology and Management, 534, 120853. DOI: https://doi.org/10.1016/j.foreco.2023.120853

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Published

2023-06-08

How to Cite

Siagian, R. C., Alfaris, L. ., Ahmad, G. N. ., Laeiq, N. ., Muhammad, A. C. ., Nyuswantoro, U. I. ., & Nasution, B. . (2023). Relationship between Solar Flux and Sunspot Activity Using Several Regression Models. JURNAL ILMU FISIKA, 15(2), 146–165. https://doi.org/10.25077/jif.15.2.146-165.2023

Issue

Vol. 15 No. 2 (2023): September 2023

Section

Research Article

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Copyright (c) 2023 Ruben Cornelius Siagian, Lulut Alfaris, Ghulab Nabi Ahmad, Nazish Laeiq, Aldi Cahya Muhammad, Ukta Indra Nyuswantoro, Budiman Nasution

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