Abstract
Space weather and ionospheric conditions effects on the Global Satellite Navigation System (GNSS) positioning performance and operation have already been identified. However, the qualification of this relationship is still a subject of scientific activities. A model forecasting the level of GNSS positioning performance degradation caused by space weather and ionospheric dynamics represents a valuable scientific goal. This manuscript addresses the refinement in forecasting model development procedure achieved through utilisation of selected supervised machine learning method based on Linear Models (LM) and Component Analysis (PCA) on experimentally collected data set of the quiet space-weather period.
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