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Similar to the Dst index, the SYM-H index may also serve as anindicator of magnetic storm intensity, but having distinct advantage ofhigher time-resolution. In this study the NARX neural network has been usedfor the first time to predict SYM-H index from solar wind (SW) and IMFparameters. In total 73 time intervals of great storm events with IMF/SW dataavailable from ACE satellite during 1998 to 2006 are used to establish theANN model. Out of them, 67 are used to train the network and the other 6samples for test. Additionally, the NARX prediction model is also validatedusing IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and2005, as well as for the July 2000 Bastille day storm and November 2001superstorm using Geotail and OMNI data at 1 AU, respectively. Fiveinterplanetary parameters of IMF Bz, By and total B componentsalong with proton density and velocity of solar wind are used as the originalexternal inputs of the neural network to predict the SYM-H index about onehour ahead. For the 6 test storms registered by ACE including twosuper-storms of min. SYM-H<−200 nT, the correlation coefficient betweenobserved and NARX network predicted SYM-H is 0.95 as a whole, even as high as0.95 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 isalso satisfactory, showing averaged correlation coefficient about 0.91 andRMSE of 14.2 nT. The newly developed NARX model shows much better capabilitythan Elman network for SYM-H prediction, which can partly be attributed to akey 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 ringcurrent status is effectively directed to take part in the prediction ofSYM-H index by ANN. The proper history length of the output-feedback maymainly reflect on average the characteristic time of ring current decay whichinvolves various decay mechanisms with ion lifetimes from tens of minutes totens of hours. The Elman network makes feedback from hidden layer to inputonly one step, which is of 5 min for SYM-H index in this work and thusinsufficient to catch the characteristic time length

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