Prediction of Geomagnetic Storm Using Neural Networks - TWiki

XV Chilean Physics Symposium, 2006 Journal of Physics: Conference Series 134 (2008) 012041

IOP Publishing doi:10.1088/1742-6596/134/1/012041

Prediction of Geomagnetic Storm Using Neural Networks: Comparison of the Efficiency of the Satellite and GroundBased Input Parameters Marina Stepanova1,2, Elizavieta Antonova3, F.A. Muños-Uribe2, S.L. GomezGordo2, M.V. Torres-Sanchez2 1

Physical Department, Universidad de Santiago de Chile Chilean Air Force Aeronautic Polytecnic Academy, Santiago, Chile, 3 Skobeltsyn Institute of Nuclear Physics, Moscow State University, Moscow, Russia 2

E-mail: [email protected] Abstract. Different kinds of neural networks have established themselves as an effective tool in the prediction of different geomagnetic indices, including the Dst being the most important constituent for determination of the impact of Space Weather on the human life. Feed-forward networks with one hidden layer are used to forecast the Dst variation, using separately the solar wind paramenters, polar cap index, and auroral electrojet index as input parameters. It was found that in all three cases the storm-time intervals were predicted much more precisely as quite time intervals. The majority of cross-correlation coefficients between predicted and observed Dst of strong geomagnetic storms are situated between 0.8 and 0.9. Changes in the neural network architecture, including the number of nodes in the input and hidden layers and the transfer functions between them lead to an improvement of a network performance up to 10%.

1. Introduction Different types of neural networks have established themselves as effective tools in the prediction of time series behavior, including the space weather forecasting. Several models have been developed for the prediction of the ring current index Dst, despite difficulties in its determination [1], including different kinds of neural networks [2-4]. In particular, it was found that good prediction of the storm recovery phase requires knowledge about previous values of the Dst index. The polar cap (PC) index was used in [5] for the Dst prediction using the time-delay neural networks. 2. Neural Architecture Training Various types of neural networks have been used for geomagnetic activity forecast, including Elman recurrent networks [4], the radial basis function neural network [3], and the self-organized maps [6]. Feed-forward multilayer perceptrons are generally used for pattern recognition problems. However in this work the input data sets were organized as a temporal sequence. The data, sampled during a time window of duration ξ, is shown to the network simultaneously, i. e. the information about the previous stages of the magnetosphere is embedded into the input vector. This window is stepwise in time, nevertheless it is possible to present the learning patterns randomly. Therefore the presence of

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data gaps does not affect the learning process as it does in the case of recurrent networks. This kind of works was named time-delay neural network [3,5,7]. The architecture of a feed-forward network is specified by the number of neurons used in the input, hidden, and output layers. The output Oiµ of a single hidden-layer neural network with an input pattern ξ is given by    Oiµ = g1  ∑ wij g 2  ∑ w jk ξ kµ    k   j

(1)

where wij and w jk are the weights between the input and hidden layers and between the hidden and the output layers, respectively, g1,2 are the transfer functions. We used 8 neurons in the input vector and linear scale function as a scaling function to scale all input values into an interval . The double brackets indicate that larger numbers are allowed later when we apply the neural network to the validation set inputs, conserving the same scaling factor as in case of training and validation sets. Different numbers of neurons in the hidden layer have been checked to establish the best network configuration. The logistic f ( x) = 1/ (1 + exp(− x) ) function was used as the transfer one. The weights were updated by the scaled conjugate gradient method. The product between the solar wind bulk velocity z component of the interplanetary magnetic field VBz, and the PC index during 1999 and 2000 were divided into training, test, and validation data subsets. The accuracy of predictions was estimated by calculating the linear prediction-target correlation coefficient as N (2) ∑ µ =1 (T µ − T )( O µ − O ) ρ=

∑ µ (T µ − N

=1

T

) ∑ (O 2

N

µ =1

µ

− O

)

2

where T is the target vector. 3. Results The performance of the networks was checked by applying the neural networks to predict the Dst variation during 16 storm time intervals in July-December 2000. We also explored the possibility to use the auroral electrojet indices for this pburpose. It was found that the use of the AL-index gives considerably better results. The definitive AL index is available up to 1988 only, therefore it was not possible to select the same data sets for AL, PC and VBz. Fig. 1, 2 and 3 show the relationship between the observed and predicted Dst minima. As it can be seen, the minimum values of the Dst variation are predicted correctly in case of the VBz input and are underestimated in case of PC index for strong storms. In case of AL index, the Dst minimum was also reproduced, but with great spreading from storm to storm. However, we also obtained that the cross-correlation coefficient between observed and predicted value of Dst variation is almost the same for strong and weak geomagnetic storms. Fig. 4 and 5 show the observed and predicted Dst variation for the strongest storm of the validation data set, occurred August 10-15, 2000. As it can be seen, the amplitude of the Dst variation is well predicted in the first case and it is underestimated in the second one. However, this underestimation does not occur generally when the Dst amplitude does not exceed -100 nT. Fig. 6 shows the same variations, obtained for XXX 1981 strom, using AL index. This indicates that, in genera, AL index reproduce better strong geomagnetic storms. Six geomagnetic storms with Dst-150, we have more scattering values of ρ. Another feature of the use of AL index is the better reproduction of the recovery phase (Fig. 5) then in the case of using PC and VBz.

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4. Discussion The use of the solar wind VBz and the PC index for Dst prediction is physically connected to the convective transport of plasma sheet particles into the ring current during geomagnetic storms. The high level of correlation correlation of VBz with large-scale magnetospheric electric field is well known. PC index is linearly correlated with the cross polar cap voltage [10] under conditions of low or moderate magnetic activity PC 10) the polar cap electric field tends to saturate at Eion≈ 40-45 mV/m. It would be stressed that the PC index is sensitive not only to VBz in the solar wind, but to a number of factors influencing activity in the polar cap. The relationship between the polar cap potential and the PC-index can be more complicated. The PC index is also strongly affected by solar wind pressure pulses [9], and PC contain the information not only on VBz, but also on the solar wind dynamic pressure. On the other site, the auroral AL index is used for the description of substorm activity. Therefore it contains not only information on the large-scale electrostatic electric fields, but also on impulsive electric fields producing substorm pressure injections in the inner magnetosphere. 5. Conclusions To date the most promising techniques of the space weather forecast are based on the study the early precursors of geomagnetic storms, like the prediction of daily solar wind velocity days ahead. However, sometimes the solar wind properties in the libration point differ significantly from those measured in the vicinity of the magnetosphere [10]. Sometimes also there are gaps in the satellite data. Therefore, it is reasonable to develop an alternative secondary predicting methods relying solely on ground-based measurements. AL index is a good tool of Dst prediction, but it is not available in real time. The PC index seems to be a good candidate and it is the only index available now in real time. However, to make the PC index based Dst forecast more reliable, it will be necessary to improve the quantitative understanding of variations in ionospheric conductivity. PC index as an input parameter for the time-delayed neural networks gives the possibility to predict Dst values up to -100 nT. We suggest that the proper inclusion of the observed nonlinearity of the PC index and the simultaneous use of PC index from both hemispheres will lead to an increase in the accuracy of the prediction of Dst for great geomagnetic storms. Acknowledgments The research was supported by Chilean Airforce Polytechnic Academy, DICYT(USACH), INTAS grant 03-51-3738, RFBR grant 05-05-64394-a and the program Universities of Russia.

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