scholarly journals Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks

1997 ◽  
Vol 102 (A7) ◽  
pp. 14255-14268 ◽  
Author(s):  
Jian‐Guo Wu ◽  
Henrik Lundstedt
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Wind Data ◽  
Dynamic Neural Networks

scholarly journals Predicting geomagnetic storms from solar-wind data using time-delay neural networks

1996 ◽  
Vol 14 (7) ◽  
pp. 679-686 ◽  
Author(s):  
H. Gleisner ◽  
H. Lundstedt ◽  
P. Wintoft
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Time Delay ◽  
Input Data ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Time History ◽  
Recovery Phase ◽  
Wind Data ◽  
Data Sequence

Abstract. We have used time-delay feed-forward neural networks to compute the geomagnetic-activity index Dst one hour ahead from a temporal sequence of solar-wind data. The input data include solar-wind density n, velocity V and the southward component Bz of the interplanetary magnetic field. Dst is not included in the input data. The networks implement an explicit functional relationship between the solar wind and the geomagnetic disturbance, including both direct and time-delayed non-linear relations. In this study we especially consider the influence of varying the temporal size of the input-data sequence. The networks are trained on data covering 6600 h, and tested on data covering 2100 h. It is found that the initial and main phases of geomagnetic storms are well predicted, almost independent of the length of the input-data sequence. However, to predict the recovery phase, we have to use up to 20 h of solar-wind input data. The recovery phase is mainly governed by the ring-current loss processes, and is very much dependent on the ring-current history, and thus also the solar-wind history. With due consideration of the time history when optimizing the networks, we can reproduce 84% of the Dst variance.

scholarly journals Probabilistic Geomagnetic Storm Forecasting via Deep Learning

2021 ◽  
Author(s):  
Adrian Tasistro-Hart ◽  
Alexander Grayver ◽  
Alexey Kuvshinov
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Solar Disk ◽  
Geomagnetic Storms ◽  
Probabilistic Forecasts ◽  
Uncertainty Estimates ◽  
External Component ◽  
Almost All ◽  
Communication Lines

<p>By causing time variation in Earth's external magnetic field, geomagnetic storms can induce damaging currents in ground-based conducting infrastructure, such as power and communication lines.  The physical link between solar activity and Earth's magnetosphere, while complicated, provides the basis for attempts to forecast geomagnetic storms. Fortunately, we have abundant observational data of both the solar disk and solar wind, which are ameable to the application of data-hungry neural networks to the forecasting problem. To date, almost all neural networks trained for geomagnetic storm forecasting have utilized solar wind observations from the Earth-Sun first Lagrangian point (L1) or closer and have generated deterministic output without uncertainty estimates. Furthermore, existing models generate forecasts for indices that are also sensitive to induced internal magnetic fields, complicating the forecasting problem with another layer of non-linearity. In this work, we present neural networks trained on observations from both the solar disk and the L1 point. Our architecture generates reliable probabilistic forecasts over Est, the external component of the disturbance storm time index, showing that neural networks can learn measures of confidence in their output. </p>

scholarly journals Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

2010 ◽  
Vol 28 (2) ◽  
pp. 381-393 ◽  
Author(s):  
L. Cai ◽  
S. Y. Ma ◽  
Y. L. Zhou
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Correlation Coefficient ◽  
Characteristic Time ◽  
Ring Current ◽  
Wind Data ◽  
Distinct Advantage ◽  
Elman Network ◽  
H Index ◽  
Narx Neural Network

Abstract. Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW) and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF Bz, By and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H<−200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min). This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may mainly reflect on average the characteristic time of ring current decay which involves various decay mechanisms with ion lifetimes from tens of minutes to tens of hours. The Elman network makes feedback from hidden layer to input only one step, which is of 5 min for SYM-H index in this work and thus insufficient to catch the characteristic time length.

scholarly journals A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Exponential Stability ◽  
External Disturbance ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Input Output ◽  
Lmi Approach ◽  
Dynamic Neural Networks ◽  
Numerical Examples

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

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