Classification Approach to Prediction of Geomagnetic Disturbances

Gadzhiev, I.M.; Isaev, I.V.; Barinov, O.G.; Dolenko, S.A.; Myagkova, I.N.

Авторы: Gadzhiev I.M., Isaev I.V., Barinov O.G., Dolenko S.A., Myagkova I.N.
Журнал: Moscow University Physics Bulletin
Том: 78
Номер: Suppl.1
Год издания: 2023
Издательство: Allerton Press
Местоположение издательства: New York, N.Y., United States
Первая страница: S96
Последняя страница: S103
DOI: 10.3103/S002713492307007X
Аннотация: 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 planetary index Kp, which is provided at 3-hour interval. The approach used in this study implies classifying geomagnetic disturbances according to the level of the Kp 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 L1 Lagrange point between the Sun and the Earth, and of the value of Kp 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 pre-processing 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 classes is partially reduced using SMOTE technique. It is demonstrated that the suggested approach outperforms the trivial inertial model for all values of prediction horizon from 3 to 24 hours (with 3 hours step). The most efficient pre-processing methods are described, as well as the best machine learning models.
Добавил в систему: Доленко Сергей Анатольевич

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