| Autor: |
Sabrina Guastavino, Katsiaryna Bahamazava, Emma Perracchione, Fabiana Camattari, Gianluca Audone, Daniele Telloni, Roberto Susino, Gianalfredo Nicolini, Silvano Fineschi, Michele Piana, Anna Maria Massone |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
The Astrophysical Journal, Vol 971, Iss 1, p 94 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1538-4357 |
| DOI: |
10.3847/1538-4357/ad5b57 |
| Popis: |
This study addresses the prediction of geomagnetic disturbances by exploiting machine learning techniques. Specifically, the Long Short-term Memory recurrent neural network, which is particularly suited for application over long time series, is employed in the analysis of in situ measurements of solar wind plasma and magnetic field acquired over more than one solar cycle, from 2005 to 2019, at the Lagrangian point L1. The problem is approached as a binary classification aiming to predict 1 hr in advance a decrease in the SYM-H geomagnetic activity index below the threshold of −50 nT, which is generally regarded as indicative of magnetospheric perturbations. The strong class imbalance issue is tackled by using an appropriate loss function tailored to optimize appropriate skill scores in the training phase of the neural network. Beside classical skill scores, value-weighted skill scores are then employed to evaluate predictions, suitable in the study of problems, such as the one faced here, characterized by strong temporal variability. For the first time, the content of magnetic helicity and energy carried by solar transients, associated with their detection and likelihood of geoeffectiveness, were considered as input features of the network architecture. Their predictive capabilities are demonstrated through a correlation-driven feature selection method to rank the most relevant characteristics involved in the neural network prediction model. The optimal performance of the adopted neural network in properly forecasting the onset of geomagnetic storms, which is a crucial point for giving real warnings in an operational setting, is finally showed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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