This paper presents a novel Machine Learning (ML) approach to ionospheric forecasting, including forecasting the space weather impact on the ionosphere. It exploits a data-driven approach in which the models learn underlying processes and relationships from data describing solar activity, solar wind, interplanetary and Earth's magnetic fields, and the ionosphere. We applied a multi-model and multi-data ensemble forecasting approach using diverse models of different learning algorithms with different training datasets to generate 1-day VTEC forecasts. This approach improved forecasting accuracy compared to a single-model-based approach. In addition, the forecast uncertainty of the super-ensemble model was assessed by estimating an ensemble spread. The results show potential for forecasting VTEC in different ionospheric regions during quiet and storm periods while quantifying their uncertainties.     «

This paper presents a novel Machine Learning (ML) approach to ionospheric forecasting, including forecasting the space weather impact on the ionosphere. It exploits a data-driven approach in which the models learn underlying processes and relationships from data describing solar activity, solar wind, interplanetary and Earth's magnetic fields, and the ionosphere. We applied a multi-model and multi-data ensemble forecasting approach using diverse models of different learning algorithms with diffe...     »