Journal of The Korean Astronomical Society (천문학회지)

The Korean Astronomical Society (한국천문학회)
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SUNSPOT AREA PREDICTION BASED ON COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND EXTREME LEARNING MACHINE

  • Peng, Lingling (School of Artificial Intelligence, Chongqing University of Arts and Sciences)
  • Received : 2020.07.19
  • Accepted : 2020.11.05
  • Published : 2020.12.31
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Abstract

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

Keywords

Acknowledgement

This work is supported by the Science and Technology Research Program of the Chongqing Municipal Education Commission of China (Grant No. KJQN201901306, KJQN201901350, KJQN201801325).

References

  1. Abdel-Rahman, H. I., & Marzouk, B. A. 2018, Statistical Method to Predict the Sunspots Number, NRIAG-JAG, 7, 175
  2. Akhavan-Amjadi, M. 2020, Fetal Electrocardiogram Modeling Using Hybrid Evolutionary Firefly Algorithm and Extreme Learning Machine, MSSP, 31, 117
  3. Alejandro, M. R., Smith, K. L., & Roberts, P. C. E. 2018, Solar Activity Simulation and Forecast with a Fluxtransport Dynamo, MNRAS, 479, 3791 https://doi.org/10.1093/mnras/sty1625
  4. Andreeva, O.A., Abramenko, V.I. & Malaschuk, V.M. 2020, Coronal Holes during the Period of Maximum Asymmetry in the 24th Solar Activity Cycle, Astrophys., 63, 114 https://doi.org/10.1007/s10511-020-09619-2
  5. Bhowmik, P., & Nandy, D. 2018, Prediction of the Strength and Timing of Sunspot Cycle 25 Reveal Decadal-scale Space Environmental Conditions, Nat. Commun., 9, 1 https://doi.org/10.1038/s41467-017-02088-w
  6. Bruevich, E. A., & Bruevich, V. V. 2018, Powerful Solar Flares in September 2017. Comparison with the Largest Flares in Cycle 24, Astrophys., 61, 241 https://doi.org/10.1007/s10511-018-9531-z
  7. Chae, J., & Kim, Y. H. 2017, Performance of the Autoregressive Method in Long-term Prediction of Sunspot Number, JKAS, 50, 21
  8. Chang, H. Y. 2015, Latitudinal Distribution of Sunspots and Duration of Solar Cycles, JKAS, 48, 325
  9. Cho, I. H., Bong, S. C., Cho, K. S., et al. 2014, Statistical Study on Personal Reduction Coefficients of Sunspot Numbers since 1981, JKAS, 47, 255
  10. Covas, E., Peixinho, N., & Fernandes, J. 2019, Neural Network Forecast of the Sunspot Butterfly Diagram, Sol. Phys., 294, 24 https://doi.org/10.1007/s11207-019-1412-z
  11. Dean, P. W.,& Schatten, K. H.. 2018, An Early Prediction of the Amplitude of Solar Cycle 25, Sol. Phys., 293, 112 https://doi.org/10.1007/s11207-018-1330-5
  12. Deng, L. H., Xiang, Y. Y., Qu, Z. N., & An, J. M. 2016, Systematic Regularity of Hemispheric Sunspot Areas over the Past 140 Years, AJ, 151, 70 https://doi.org/10.3847/0004-6256/151/3/70
  13. Deng H., Mei Y., & Wang F. 2020, Periodic Variation and Phase Analysis of Grouped Solar Flare with Sunspot Activity, RAA, 20, 022
  14. Ding, L. G., Lan, R. S., Jiang, Y., & Peng, J. D. 2012, Prediction of the Smoothed Monthly Mean Sunspot Area Based on Neural Network, Trans. Atmosph. Sci., 35, 508
  15. Donskikh, G. I., Ryabov, M. I., Sukharev, A. L., & Aller, M. 2016, Singular-spectrum Analysis and Wavelet Analysis of the Variability of the Extragalactic Radio Sources 3C 120 and CTA 102, Astrophys., 59, 199 https://doi.org/10.1007/s10511-016-9427-8
  16. Florios, K., Kontogiannis, I., Park, S. H., et al. 2018, Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning, Sol. Phys., 293, 28 https://doi.org/10.1007/s11207-018-1250-4
  17. Gong, B. M., Wang, W. B., & Zhao, P. 2014, EMD-FSVM Prediction for Nonstationary Time Series, Computer Sci., 041, 57
  18. Gopasyuk, O. S. 2017, Magnetic Field of Sunspots during the Rising Phase of Activity Cycle 24, Astrophys., 60, 90 https://doi.org/10.1007/s10511-017-9465-x
  19. Guo, Y., Pariat, E., Valori, G., et al. 2017, Magnetic Helicity Estimations in Models and Observations of the Solar Magnetic Field. III. Twist Number Method, ApJ, 840, 40 https://doi.org/10.3847/1538-4357/aa6aa8
  20. Hamid, R. H., & Marzouk, B. A. 2018, Forecasting the Peak of the Present Solar Activity Cycle 24, NRIAG-JAG, 7, 15
  21. Hathaway, D. H. 2015, The Solar Cycle, Living Rev. Sol. Phys., 12, 4 https://doi.org/10.1007/lrsp-2015-4
  22. Huang, N. E., Shen, Z., Long, S. R., et al. 1998, The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis, Proc. Roy. Soc. London A, 454, 903 https://doi.org/10.1098/rspa.1998.0193
  23. Huang, G. B., Zhou, H., Ding, X., & Zhang, R. 2012, Extreme Learning Machine for Regression and Multiclass Classification, IEEE Transactions on Systems Man & Cybernetics Part B, 42, 513 https://doi.org/10.1109/TSMCB.2011.2168604
  24. Inceoglu, F., Simoniello, R., Arlt, R., & Rempel, M. 2019, Constraining Non-linear Dynamo Models Using Quasibiennial Oscillations from Sunspot Area Data, A&A, 625, A117 https://doi.org/10.1051/0004-6361/201935272
  25. Jeong, E. J., Lee, J. Y., Moon, Y. J., & Park, J. 2014, Forecast of Solar Proton Events with NOAA Scales based on Solar X-ray Flare Data Using Neural Network, JKAS, 47, 209
  26. Kuzanyan, K. M., Safiullin, N., Kleeorin, N., et al. 2019, Large-Scale Properties of the Tilt of Sunspot Groups and Joy's Law Near the Solar Equator, Astrophys. 62, 261 https://doi.org/10.1007/s10511-019-09579-2
  27. Labonville, F., Charbonneau, P., & Lemerle, A. 2019, A Dynamo-based Forecast of Solar Cycle 25, Sol. Phys., 294, 82 https://doi.org/10.1007/s11207-019-1480-0
  28. Lee, J. Y., Moon, Y. J., Kim, K. S., et al. 2007, Prediction of Daily Maximum X-ray Flux Using Multilinear Regression and Autoregressive Time-series Methods, JKAS, 40, 99
  29. Li, F. F. , Wang, S. Y., & Wei, J. H. 2018, Long Term Rolling Prediction Model for Solar Radiation Combining Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) techniques, JRSE, 10, 013704
  30. Li, G., & Wang, S. 2017, Sunspot Time-series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network, MPE, 2017, 1
  31. Liu, H., Liu, C., Wang, J. T., & Wang, H. 2019, Predicting Solar Flares Using a Long Short-term Memory Network, ApJ, 877, 121 https://doi.org/10.3847/1538-4357/ab1b3c
  32. Macario-Rojas, A., Smith, K. L., & Roberts, P. C. 2018, Solar Activity Simulation and fForecast with a Fluxtransport Dynamo, MNRAS, 479, 3791 https://doi.org/10.1093/mnras/sty1625
  33. Miao, J., Wang, X., Ren, T. L., & Li, Z. T. 2020, Prediction Verification of Solar Cycles 18-24 and a Preliminary Prediction of the Maximum Amplitude of Solar Cycle 25 Based on the Precursor Method, RAA, 20, 4
  34. Miller, T. H., Gallidabino, M. D., Macrae, J. R., et al. 2019, Prediction of Bioconcentration Factors in Fish and Invertebrates Using Machine Learning, Sci. Total Environ., 648, 80 https://doi.org/10.1016/j.scitotenv.2018.08.122
  35. Pala, Z., & Atici, R. 2019, Forecasting Sunspot Time Series Using Deep Learning Methods, Sol. Phys., 294, 50 https://doi.org/10.1007/s11207-019-1434-6
  36. Pesnell, W. D.,& Schatten, K. H. 2018, An Early Prediction of the Amplitude of Solar Cycle 25, Sol. Phys., 293, 112 https://doi.org/10.1007/s11207-018-1330-5
  37. Ravindra, B., & Javaraiah, J. 2015, Hemispheric Asymmetry of Sunspot Area in Solar Cycle 23 and Rising Phase of solar Cycle 24: Comparison of Three Data Sets, New Astron., 39, 55 https://doi.org/10.1016/j.newast.2015.03.004
  38. Saadi, S., & Chaker, A. 2018, A Numerical Simulation Approach for Sunspot Area Calculation, ETASR, 8, 3013
  39. Sarp, V., Kilcik, A., Yurchyshyn, V., et al. 2018, Prediction of Solar Cycle 25: A Non-linear Approach, MNRAS, 481, 2981 https://doi.org/10.1093/mnras/sty2470
  40. Stenflo, J. O. 2017, History of Solar Magnetic Fields since George Ellery Hale, SSR, 210, 5
  41. Sukharev, A. L., Ryabov, M. I. & Donskikh, G. I. 2016, Predicting Changes in the Radio Emission Fluxes of Extragalactic Sources, Astrophys., 59, 213 https://doi.org/10.1007/s10511-016-9428-7
  42. Tang, J. 2015, Application of the Grey Topological Theory in the Prediction of Yearly Mean Sunspot Numbers, ChA&A, 39, 45
  43. Tang, L., Dai, W., Yu, L., & Wang, S. 2015, A Novel CEEMD-based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting, IJITDM, 14, 141
  44. Tang, L., Wu, Y., & Yu, L. 2018, A randomized-algorithmbased Decomposition Ensemble Learning Methodology for Energy Price Forecasting, Energy, 157,526 https://doi.org/10.1016/j.energy.2018.05.146
  45. Vorobeva, E. 2019, Notes on the Correlation between Sudden Stratospheric Warmings and Solar Activity, ANGEO, 37, 375
  46. Wang, H., & Li, G. 2019, Extreme Learning Machine Cox Model for High-dimensional Survival Analysis, Statist. Medicine, 38, 2139 https://doi.org/10.1002/sim.8090
  47. Warren, H. P., Emmert, J. T., & Crump, N. A. 2017, Linear Forecasting of the F 10.7 Proxy for Solar Activity, Space Weather, 15, 1039 https://doi.org/10.1002/2017SW001637
  48. Wilson, R. M. 2016, Sunspot Area in Relation to the Newly Revised Sunspot Number, JAAS, 87, 146
  49. Wu, Z., & Huang, N. E. 2009, Ensemble Empirical Mode Decomposition: A Noise-assisted Data Analysis Method, AADA, 1, 1
  50. Yadav, H. K., Pal, Y., & Tripathi, M. M. 2020, Short-term PV Power Forecasting Using Empirical Mode Decomposition in Integration with Back-propagation Neural Network, JIOS, 41, 25
  51. Yeh, J. R., Shieh, J. S., & Huang, N. E. 2010, Complementary Ensemble Empirical Mode Decomposition: A Novel Noise Enhanced Data Analysis Method, AADA, 2, 135