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Authors: Zhelavskaya Irina S., Shprits Yuri Y, and ć Maria
Title: Empirical modeling of the plasmasphere dynamics using neural networks
Abstract: We propose a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent datasets withheld from the training set and by comparing the model predicted global evolution with global images of He+ distribution in the Earth's plasmasph. . .
Date: 10/2017 Publisher: Journal of Geophysical Research: Space Physics DOI: 10.1002/2017JA024406 Available at:
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