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In recent years (2000-2021), human-space activities have been increasingfaster than ever. More than 36000 Earth' orbiting objects, all larger than 10cm, in orbit around the Earth, are currently tracked by the European SpaceAgency (ESA). Around 70\% of all cataloged objects are in Low-Earth Orbit(LEO). Aerodynamic drag provides one of the main sources of perturbations inthis population, gradually decreasing the semi-major axis and period of the LEOsatellites. Usually, an empirical atmosphere model as a function of solar radioflux and geomagnetic data is used to calculate the orbital decay and lifetimesof LEO satellites. In this respect, a good forecast for the space weather datacould be a key tool to improve the model of drag. In this work, we proposeusing Time Series Forecasting Model to predict the future behavior of the solarflux and to calculate the atmospheric density, to improve the analytical modelsand reduce the drag uncertainty.

Machine learning to predict the solar flux and geomagnetic indices to model density and Drag in Satellites