Classification Approach to Prediction of Geomagnetic Disturbances

• Published in: Moscow University physics bulletin 2023-12, Vol.78 (Suppl 1), p.S96-S103
• Main Authors: Gadzhiev, I. M., Isaev, I. V., Barinov, O. G., Dolenko, S. A., Myagkova, I. N.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Disturbances; Electrical networks; Geomagnetism; Interplanetary magnetic field; Lagrangian equilibrium points; Machine learning; Machine Learning in Natural Sciences; Magnetic storms; Mathematical and Computational Physics; Mathematical models; Parameters; Physics; Physics and Astronomy; Power lines; Preprocessing; Radio communications; Solar magnetic field; Solar wind; Theoretical; Time series
• ISSN: 0027-1349; 1934-8460
• DOI: 10.3103/S002713492307007X

Magnetic storms can cause disruptions in the operation of radio communications, pipelines, power lines, and electrical networks, and they may possibly cause human health problems. Therefore, prediction of geomagnetic disturbances is of great practical value. Geomagnetic disturbances are usually described with the help of geomagnetic indices, including the planetary index which is provided at a 3-h interval. The approach used in this study implies classifying geomagnetic disturbances according to the level of the index. To do so, the whole range of the index values is divided into several intervals according to the degree of disturbance. The input data are time series of parameters of solar wind and interplanetary magnetic field, measured onboard spacecraft at the L1 Lagrange point between the Sun and the Earth, aa well as the value of the index itself. To account for the ‘‘memory’’ of the time series, delay embedding of all the parameters is used—for each of the parameters, its several preceding values are taken into account. Additional preprocessing of the parameters is performed by calculating moving averages and other statistical indicators of the time series. To perform classification, various machine learning methods such as gradient boosting and artificial neural networks are used. The optimal values of the parameters of each method are determined by cross-validation, and pattern misbalance among the classes is partially reduced using the SMOTE technique. It is demonstrated that the suggested approach outperforms the trivial inertial model for all the values of the prediction horizon from 3 to 24 h (with a 3-h step). The most efficient preprocessing methods are described, as well as the best machine learning models.