Objective discrimination of geomagnetic disturbances and prediction of Dst index by artificial neural networksстатья

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[1] Objective discrimination of geomagnetic disturbances and prediction of dst index by artificial neural networks / S. A. Dolenko, I. N. Myagkova, V. R. Shiroky, I. G. Persiantsev // Proceedings of the 10th International Conference “Problems of Geocosmos”. — St. Petersurg, 2014. — P. 270–275. Strong disturbances of the Earth’s magnetic field (geomagnetic storms) may have significant effect upon operation of engineering devices and well-being of people. Therefore, prediction of the state of magnetosphere is a very important problem. Geomagnetic disturbances (or magnetic storms) are one of important factors of space weather. In this study, we suggest an algorithm for objective discrimination of boundaries and different phases of magnetic storms based on the time series of hourly values of Dst-index. Two or three phases were marked for each storm: initial phase (optional), main phase, and recovery phase. With the help of the suggested algorithm, the boundaries of magnetic storms and their phases for the period from November 1997 till March 2014 have been marked automatically in an objective way. Then, neural network prediction of the value of Dst index by the parameters of solar wind and interplanetary magnetic field in L1 point and by preceding values of Dst index itself, has been performed. In this study, we perform detailed analysis of the results obtained for storm data, and suggest ways of improving existing approaches to neural network prediction of Dst-index.

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