Abstract:
Artificial Neural Network (ANN) has proven to be very successful in forecasting a variety of
irregular magnetospheric/ionospheric processes like geomagnetic storms and substorms. SYMH and
ASYH indices represent longitudinal symmetric and the asymmetric component of the ring current. Here,
an attempt is made to develop a prediction model for these indices using ANN. The ring current state
depends on its past conditions therefore, it is necessary to consider its history for prediction. To account
for this effect Nonlinear Autoregressive Network with exogenous inputs (NARX) is implemented. This
network considers input history of 30 min and output feedback of 120 min. Solar wind parameters mainly
velocity, density, and interplanetary magnetic field are used as inputs. SYMH and ASYH indices during
geomagnetic storms of 1998–2013, having minimum SYMH < 85 nT are used as the target for training
two independent networks. We present the prediction of SYMH and ASYH indices during nine geomagnetic
storms of solar cycle 24 including the recent largest storm occurred on St. Patrick’s day, 2015. The
present prediction model reproduces the entire time profile of SYMH and ASYH indices along with small
variations of ~10–30 min to the good extent within noise level, indicating a significant contribution of interplanetary
sources and past state of the magnetosphere. Therefore, the developed networks can predict
SYMH and ASYH indices about an hour before, provided, real-time upstream solar wind data are available.
However, during the main phase of major storms, residuals (observed-modeled) are found to be large,
suggesting the influence of internal factors such as magnetospheric processes.
