Using Machine Learning to Predict the Density of Solar Winds

Abstract

Solar winds are a variable flow of ions originating from the Sun’s corona that have the ability to travel to Earth and disrupt its magnetic field, leading to unpredictable space weather storms which can disturb communication systems and power grids. Prediction and therefore preparation for these solar winds can ensure stability in everyday life; however, an accurate predictor for solar wind features has yet to be developed. Previous models for solar wind prediction have been inhibited by incomplete datasets, correlated features, and over-simplification of solar winds. In this study, a linear regression model was fit to predict the density of solar winds using machine learning. The greater the density of a certain solar wind event, the greater the chance of collision with Earth’s magnetosphere, and therefore ramifications on Earth, due to the presence of more charged particles per unit space within the solar wind. The dataset used in this study was taken from a public NASA/NOAA dataset which included 1,048,575 solar wind events. After data preprocessing was performed, the working dataset was composed of 792,587 solar winds events. The model was then trained using the Scikit-Learn Python library to determine a linear regression equation. Following three different training strategies, the model had a relatively low MAE and RMSE score, demonstrating low variance between the true and predicted values of solar wind density. In the future, this model could be applied to real-time input data to warn against concerningly high solar wind density which may negatively impact life on Earth.