Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms

[1]  Annette Witt,et al.  Quantification of Long-Range Persistence in Geophysical Time Series: Conventional and Benchmark-Based Improvement Techniques , 2013, Surveys in Geophysics.

[2]  Heinrich Schwabe,et al.  Sonnen — Beobachtungen im Jahre 1843 , 1844 .

[3]  Wenbin Wang,et al.  Geospace system responses to the St. Patrick's Day storms in 2013 and 2015 , 2017 .

[4]  Stephen A. Billings,et al.  Data derived NARMAX Dst model , 2011 .

[5]  Enrico Camporeale,et al.  The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting , 2019, Space Weather.

[6]  Daniel T. Welling,et al.  Geospace environment modeling 2008–2009 challenge: Dst index , 2013 .

[7]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[8]  R. Singh,et al.  Space Weather: Physics, Effects and Predictability , 2010 .

[9]  Lahcen Ouarbya,et al.  The use of sequential recurrent neural filters in forecasting the Dst index for the strong magnetic storm of autumn 2003 , 2012, Appl. Math. Lett..

[10]  M. Laurenza,et al.  Persistence in recurrent geomagnetic activity and its connection with Space Climate , 2010 .

[11]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[12]  Babak Nadjar Araabi,et al.  Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling , 2006 .

[13]  Dong-Hun Lee,et al.  Comparison of Dst Forecast Models for Intense Geomagnetic Storms , 2012 .

[14]  Z. Voros,et al.  Neural network prediction of geomagnetic activity: a method using local H\"{o}lder exponents , 2004, physics/0411062.

[15]  Ramkumar Bala,et al.  Improvements in short‐term forecasting of geomagnetic activity , 2012 .

[16]  Ahmed Lethy,et al.  Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network , 2018, Space Weather.

[17]  Juan A. Lazzús,et al.  Forecasting the Dst index using a swarm‐optimized neural network , 2017 .

[18]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[19]  S. Kugblenu,et al.  Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm , 1999 .

[20]  Albert Y. Zomaya Handbook of Nature-Inspired and Innovative Computing - Integrating Classical Models with Emerging Technologies , 2006 .

[21]  C. Russell,et al.  An empirical relationship between interplanetary conditions and Dst , 1975 .

[22]  Arpan Kumar Kar,et al.  Bio inspired computing - A review of algorithms and scope of applications , 2016, Expert Syst. Appl..

[23]  Kunihiro Keika,et al.  Pileup accident hypothesis of magnetic storm on 17 March 2015 , 2015 .

[24]  Gaurav Singh,et al.  A study on precursors leading to geomagnetic storms using artificial neural network , 2016, Journal of Earth System Science.

[25]  J. Chao,et al.  Influence of the solar wind dynamic pressure on the decay and injection of the ring current , 2003 .

[26]  D. Hathaway The Solar Cycle , 2010, Living reviews in solar physics.

[27]  J. Luhmann,et al.  How unprecedented a solar minimum? , 2010 .

[28]  Pedro Vega-Jorquera,et al.  GA-optimized neural network for forecasting the geomagnetic storm index , 2018, Geofísica Internacional.

[29]  Marina Stepanova,et al.  Prediction of Dst variations from Polar Cap indices using time-delay neural network , 2005 .

[30]  K. Mursula,et al.  Modeling the contributions of ring, tail, and magnetopause currents to the corrected Dst index , 2010 .

[31]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[32]  V. Munsami,et al.  Determination of the effects of substorms on the storm‐time ring current using neural networks , 2000 .

[33]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[34]  Enrico Camporeale,et al.  Multiple‐Hour‐Ahead Forecast of the Dst Index Using a Combination of Long Short‐Term Memory Neural Network and Gaussian Process , 2018, Space Weather.

[35]  R. McPherron,et al.  Forecasting the ring current index Dst in real time , 2000 .

[36]  D. Jankovičová,et al.  Neural network-based nonlinear prediction of magnetic storms , 2002 .

[37]  P. Wintoft,et al.  Prediction of geomagnetic storms from solar wind data with the use of a neural network , 1994 .

[38]  N. Ganushkina,et al.  Contribution from different current systems to SYM and ASY midlatitude indices , 2014 .

[39]  Gabriela Andrejková,et al.  Neural networks using Bayesian training , 2003, Kybernetika.

[40]  Jian Yang,et al.  Genetic algorithm optimized training for neural network spectrum prediction , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).

[41]  W. Pesnell,et al.  Predicting Solar Cycle 24 Using a Geomagnetic Precursor Pair , 2014 .

[42]  Xinlin Li,et al.  A new model for the prediction of Dst on the basis of the solar wind , 2002 .

[43]  H. Gleisner,et al.  Predicting geomagnetic storms from solar-wind data using time-delay neural networks , 1996 .

[44]  K. Kusano,et al.  No Major Solar Flares but the Largest Geomagnetic Storm in the Present Solar Cycle , 2015 .

[45]  G. Consolini,et al.  Geomagnetic D st index forecast based on IMF data only , 2006 .

[46]  H. Lundstedt,et al.  Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks , 1996 .

[47]  R. Lepping,et al.  The first super geomagnetic storm of solar cycle 24: “The St. Patrick’s day event (17 March 2015)” , 2016, Earth, Planets and Space.

[48]  L. Burlaga,et al.  Causes of sudden commencements and sudden impulses. , 1969 .

[49]  Rolland Fleury,et al.  Middle‐ and low‐latitude ionosphere response to 2015 St. Patrick's Day geomagnetic storm , 2016 .

[50]  H. W. Kroehl,et al.  What is a geomagnetic storm , 1994 .

[51]  C. Russell,et al.  On the cause of geomagnetic storms , 1974 .

[52]  N. S. Bellustin,et al.  Comparison of efficiency of artificial neural networks for forecasting the geomagnetic activity index Dst , 2000 .

[53]  E. Antonova,et al.  Forecasting of DST variations from polar cap indices using neural networks , 2005 .

[54]  N. Gopalswamy,et al.  Major solar eruptions and high-energy particle events during solar cycle 24 , 2014, Earth, Planets and Space.

[55]  Ehsan Lotfi,et al.  Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices , 2014, Neurocomputing.

[56]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[57]  Hiroshi Maeda,et al.  Impulse response of geomagnetic indices to interplanetary magnetic field. , 1979 .

[58]  Y. Feldstein,et al.  Magnetic storms and magnetotail currents , 1996 .

[59]  H. Shimazu,et al.  Prediction of the Dst index from solar wind parameters by a neural network method , 2002 .

[60]  Mike Hapgood,et al.  Towards a scientific understanding of the risk from extreme space weather , 2011 .

[61]  Shigeaki Watanabe,et al.  Operational models for forecasting Dst , 2003 .

[62]  S. Chapman,et al.  The Development of the Main Phase of Magnetic Storms , 1963 .

[63]  Erwie Zahara,et al.  A hybrid genetic algorithm and particle swarm optimization for multimodal functions , 2008, Appl. Soft Comput..

[64]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[65]  Harish Garg,et al.  A hybrid PSO-GA algorithm for constrained optimization problems , 2016, Appl. Math. Comput..

[66]  Fridrich Valach,et al.  A neural network Dst index model driven by input time histories of the solar wind–magnetosphere interaction , 2014 .

[67]  P. Wintoft,et al.  Operational forecasts of the geomagnetic Dst index , 2002 .

[68]  J. Luhmann,et al.  Geomagnetic response to magnetic clouds of different polarity , 1998 .

[69]  K. Kusano,et al.  Is Something Wrong With the Present Solar Maximum? , 2013 .

[70]  S. Andriyas,et al.  Relevance vector machines as a tool for forecasting geomagnetic storms during years 1996-2007 , 2015 .

[71]  S. Kokubun RELATIONSHIP OF INTERPLANETARY MAGNETIC FIELD STRUCTURE WITH DEVELOPMENT OF SUBSTORM AND STORM MAIN PHASE. , 1972 .

[72]  J. Dungey Interplanetary Magnetic Field and the Auroral Zones , 1961 .

[73]  R. J. Kuo,et al.  A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – A case study on supply chain model , 2011 .

[74]  M. V. Stepanova,et al.  Autoprediction of Dst index using neural network techniques and relationship to the auroral geomagnetic indices , 2012 .

[75]  J. H. Piddington Theories of the geomagnetic storm main phase , 1963 .

[76]  S. A. Billings,et al.  Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks , 2007 .

[77]  C. Fälthammar,et al.  Relationship between changes in the interplanetary magnetic field and variations in the magnetic field at the Earth's surface , 1967 .

[78]  D. Jankovičová,et al.  Nonlinear Processes in Geophysics Neural Network Prediction of Geomagnetic Activity: a Method Using Local Hölder Exponents , 2022 .

[79]  Henrik Lundstedt,et al.  Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks , 1997 .