To produce a robust forecasting of the Dst index, it is crucial to determine how the dataset is split and processed for the training and evaluation of the model. The dataset covers the period January 1990–November 2019, and includes half of the 22nd solar cycle, all of the 23rd, and almost all of the 24th. the x-axis of the GSM coordinate system is defined along the line connecting the center of the Sun to the center of the Earth the origin is defined at the center of the Earth and is positive towards the Sun the y-axis is defined as the cross product of the GSM x-axis and the magnetic dipole axis and is positive towards dusk The z-axis is defined as the cross-product of the x- and y-axes the magnetic dipole axis lies within the xz plane), the SW plasma temperature ( T), density ( D), total speed ( V), pressure (P), and east–west component of the electric field ( E y derived from B z and V x). In particular, we used hourly averages of the three components ( B x, B y, B z) of the IMF in the GSM (Geocentric Solar Magnetospheric) reference frame (i.e. The entire dataset has been obtained from the National Space Science Data Center of NASA, namely, from the OMNI database 30. The data used for the present analysis are: the solar wind (SW) plasma parameters the interplanetary magnetic field (IMF) the Dst index. 29 used IMF Bz, SW electric field, temperature, speed and density to make a prediction of the Dst index via ANN 1–12 h ahead. A better result was obtained by 12, who used SW plasma density, velocity, flow pressure and IMF components to predict the Dst index 1 h in advance. 28 forecast the Dst-index 1 h in advance. At the same time, using SW speed, density and the IMF Bz component, Gleisner et al. 23 was able to efficiently forecast the Dst-index 1–6 h ahead using its past values via artificial neural network (ANN). On the other hand, many scientists focused on the possibility to predict the Dst index via neural network (e.g. Simultaneously, other studies derived a function linking SW parameters to magnetospheric energy dynamics (e.g. Many statistical and physical models have been developed in order to forecast the Dst index using both interplanetary magnetic field (IMF) and solar wind (SW) parameters data as input ( 17 and reference therein). The Dst is an hourly index evaluated using 4 ground-based geomagnetic observatories located at low latitudes ( 15, 16 and reference therein). On the other hand, several works focused their attention on the prediction of the Dst (disturbance storm time) index 6– 13, which measures the dynamic of the symmetric part of the ring current driven by the solar activity 14. In fact, many studies have been conducted in order to definitely understand the link among solar processes, interplanetary phenomena and geomagnetic activity (e.g. Today, the possible forecasting of a geomagnetic storm represents the main topic in the space weather context. Indeed, the strategy proposed for creating datasets for training and validation plays a fundamental role in guaranteeing good performances of the proposed neural network architecture. We strongly demonstrated that the training procedure strictly changes the capability of giving correct forecasting of stormy and disturbed geomagnetic periods. To accomplish this task, we analyzed the response of a newly developed neural network using interplanetary parameters as inputs. In this scenario, we try to predict the Dst index during quiet and disturbed geomagnetic conditions using the interplanetary magnetic field and the solar wind parameters. Consequently, its accurate prediction represents one of the main subjects in space weather studies. In general, the geomagnetic activity is measured by the Dst index. The direct interaction between large-scale interplanetary disturbances emitted from the Sun and the Earth’s magnetosphere can lead to geomagnetic storms representing the most severe space weather events.
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