Prediction Capability of Geomagnetic Events from Solar Wind Data Using Neural Networks

Multiple neural network architectures, with different structural composition and complexity, are implemented in this study with the aim of providing multi-hour-ahead warnings of severe geomagnetic disturbances, based on in situ measurements of the solar wind plasma and magnetic field acquired at the Lagrangian point L1. First, a statistical analysis of the interplanetary data was performed to point out which are the most relevant parameters to be provided as input to the neural networks, and a preprocessing of the data set was implemented to face its heavy imbalance (the Earth's magnetosphere is in fact mostly at rest). Then, neural networks were tested to evaluate their performance. It turned out that, in a binary classification problem, recurrent approaches are best at predicting critical events both 1 and 8 hr in advance, achieving a balanced accuracy of 94% and 70%, respectively. Finally, in an attempt at multistep prediction of the criticality of future geomagnetic events from 1-8 hr ahead, more complex neural networks, built by merging the different types of basic convolutional and recurrent architectures, have been shown to outperform single-step and state-of-the-art approaches with a balanced accuracy of at least 70%. Interestingly, the accuracy peaks at 4 hr, corresponding to the waiting time between the detection of solar drivers at L1 and the onset of the geomagnetic storm (as previously obtained by statistical investigations), suggesting that on average this is the time the Earth's magnetosphere takes to react to the solar event.