We present a new model for the probability that the disturbance storm time (Dst) index
exceeds− 100 nT, with a lead time between 1 and 3 days. Dst provides essential information …

In this paper, an operational Dst index prediction model is developed by combining
empirical and Artificial Neural Network (ANN) models. ANN algorithms are widely used to …

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 …

A hybrid technique that combines an artificial neural network with a particle swarm
optimization (ANN+ PSO) was used to forecast the disturbance storm time (Dst) index from 1 …

In this study, we present a method that combines a Long Short‐Term Memory (LSTM)
recurrent neural network with a Gaussian process (GP) model to provide up to 6‐hr‐ahead …

In this study we apply the Support Vector Machine (SVM) combined together with Distance
Correlation (DC) to the forecasting of Dst index by using 80 intense geomagnetic storms …

In this work, we propose an artificial neural network (ANN) with seven input parameters for
the prediction of disturbance storm time (Dst) index 1 to 12 hr ahead. The ANN uses past …

The disturbance storm time (Dst) index is an important and useful measurement in space
weather research. It has been used to characterize the size and intensity of a geomagnetic …

Enhanced interaction between solar‐wind and Earth's magnetosphere can cause space
weather and geomagnetic storms that have the potential to damage critical technologies …

Abstract The Disturbance Storm Time (Dst) Index stands as a crucial geomagnetic metric,
serving to quantify the intensity of geomagnetic disturbances. The accurate prediction of the …