Neural network classification of substorm geomagnetic activity caused by solar wind magnetic clouds

Abstract A Kohonen artificial neural network (ANN) was used to classify patterns of causal relationships between the level of geomagnetic activity in the auroral zone and plasma and magnetic field parameters in the body of an interplanetary magnetic cloud (IMO). Terrestrial and satellite observations during 33rd interplanetary magnetic clouds recorded from 1998 to 2012 are examined in detail. Experiments with the ANN during its fast training show that substorm discrimination by their intensity by three classes plus a “collector” for collecting atypical events is optimal for the study. An analysis of the classification result studies showed that each selected class of substorms corresponds to a specific set of perturbations of the plasma parameters and the magnetic field of the IMO. Using the integral characteristics of the plasma and the IMF components as input parameters of the ANN allowed us to detect the levels of the expected intensity of the AL index with an accuracy of up to 70%. The created ANNs can be used to restore the AL index both during periods of isolated magnetospheric substorms and during periods of a series of continuous successive substorms, one after another.