Using Deep Learning to Achieve Short Term Business Forecast of Dst Index

Magnetic storm events triggered by solar activity can cause dramatic changes in the Earth’s magnetic field, significantly impacting the performance of systems such as communications, navigation, and power supply. These disturbances can interfere with radio signal propagation, reduce navigation accuracy, and disrupt power transmission networks. Therefore, accurately predicting magnetic storms is crucial for mitigating their effects. In space physics, the Dst index is commonly used to characterize the intensity of magnetic storms. It serves as a vital global indicator of geomagnetic activity. To enhance the prediction of magnetic storms and reduce their adverse effects, an efficient and accurate predictive model is essential. This paper proposes a magnetic storm prediction model based on Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory networks (LSTM), referred to as the C-G-LSTM model. This hybrid model leverages the strengths of CNN, GRU, and LSTM to predict the Dst index 1 to 6 h in advance, providing valuable lead time for responding to potential magnetic storm events. CNNs effectively extract spatial features from input data, while GRUs and LSTMs excel at handling time series data and capturing temporal dependencies. The performance of the C-G-LSTM model was evaluated using Dst index data provided by NASA, covering the period from 2010 to 2019. The results demonstrate that this model performs exceptionally well in predicting the Dst index. Specifically, the maximum Root Mean Square Error (RMSE) does not exceed 7.29 nT, and the Maximum Mean Absolute Error (MAE) does not exceed 5.03 nT. Although errors increase during intense magnetic activity, the model maintains high accuracy. A significant advantage of the C-G-LSTM model is that it does not require additional input parameters such as solar wind temperature, solar wind dynamic pressure, and interplanetary magnetic field components, which are often needed in other models. This makes the C-G-LSTM model more straightforward and practical for operational forecasting. Its high accuracy and efficiency in predicting magnetic storms can provide timely warnings, helping to mitigate potential impacts on communication, navigation, and power systems. In conclusion, the C-G-LSTM model represents a significant advancement in predicting magnetic storm events, offering a reliable and accurate method for forecasting the Dst index and enhancing our ability to manage the effects of solar activity on critical engineering systems.