Anastasiia A. Lebedeva, Aleksandr A. Garashchenko, Denis N. Sidorov

PJSC Sberbank, National research Irkutsk state technical university, Melentiev energy systems institute SB RAS,

The ionosphere is a part of the Earth's atmosphere with a high concentration of free electrons and ions. The characteristic features of the ionosphere include variability and heterogeneity. One of the heterogeneities is the so-called auroral oval, which determines the range of the polar lights. Recognition of the auroral oval is an important task for predicting auroral storms, since they affect the operation of long-range communication systems, navigation, communication between satellites and the ground, making it difficult or impossible. Thus, there is a need to detect and predict the movement of the auroral oval in order to be aware of the area of their possible influence in certain periods of time. Based on the available set of images obtained in the SIMuRG system, which are based on GNSS datasets, it is proposed to use the LSTM model and the CNN architecture. The paper reviews existing implementations and proposes a method for predicting auroral oval movements in images using a Convolutional LSTM architecture that combines time series processing and computer vision. The result is a machine learning model that can make predictions based on even small amounts of data.

frame prediction architecture, computer vision, machine learning, operations research