A Method for Forecasting Geomagnetic Storms Based on Deep Learning Neural Networks Using Time Series of Matrix Observations of the Uragan Muon Hodoscope
V. G. Getmanov, A. D. Gvishiani, A. A. Soloviev, K. S. Zaitsev, M. E. Dunaev, E. V. Yekhlakov
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引用次数: 0
Abstract
A method for forecasting geomagnetic storms based on deep learning neural networks using digital time series processing for matrix observations of the URAGAN muon hodoscope and scalar Dst-indices has been developed. A scheme of computational operations and extrapolation formulas for matrix observations are proposed. The a variant of the neural network software module and its parameters are chosen. A decision-making rule is formed to forecast and assess the probabilities of correct and false forecasts of geomagnetic storms. An experimental study of estimates of the probabilistic characteristics and forecasting intervals of geomagnetic storms has confirmed the efficiency of the method. The obtained forecasting results are oriented towards solving a number of solar–terrestrial physics and national economic problems.
期刊介绍:
Geomagnetism and Aeronomy is a bimonthly periodical that covers the fields of interplanetary space; geoeffective solar events; the magnetosphere; the ionosphere; the upper and middle atmosphere; the action of solar variability and activity on atmospheric parameters and climate; the main magnetic field and its secular variations, excursion, and inversion; and other related topics.