An interpretable machine learning method for forecasting the SYM-H Index | CU Experts

In this work, we develop gradient boosting machines (GBMs) for; forecasting the SYM-H index multiple hours ahead using different; combinations of solar wind and interplanetary magnetic field (IMF); parameters, derived parameters, and past SYM-H values. Using Shapley; Additive Explanation (SHAP) values to quantify the contributions from; each input to predictions of the SYM-H index from GBMs, we show that our; predictions are consistent with physical understanding while also; providing insight into the complex relationship between the solar wind; and Earth’s ring current. We also perform a direct comparison between; GBMs and neural networks presented in prior publications for forecasting; the SYM-H index by training, validating, and testing them on the same; data. We find that the GBMs have a comparable root mean squared error as; the best published black-box neural network schemes and GBMs have better; Heidke Skill Scores at predicting strong storms.

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