In this study, we compiled a data set of 510 interplanetary coronal mass ejections (ICME) events from 1996–2023 and trained a radial basis function support vector machine (RBF-SVM) model to investigate the geoeffectiveness of ICMEs and its dependence on the solar wind conditions observed at 1 au. The model demonstrates high performance in classifying geomagnetic storm intensities at specific Disturbance Storm Time thresholds and evaluating the geoeffectiveness of ICMEs. The model’s output was assessed using precision, recall, F1 score, and true skill statistics (TSS), complemented by stratified k-folds cross-validation for robustness. At the −50 nT threshold, the model achieves precisions of 0.84 and 0.93, recalls of 0.94 and 0.82, and corresponding F1 scores of 0.89 and 0.87 for the categories separated by this threshold, respectively. Overall accuracy is noted at 0.88, with a TSS of 0.76. Despite challenges at the −100 nT threshold due to data set imbalance and limited samples, the model maintains an overall accuracy of 0.87, with a TSS of 0.69, demonstrating the model’s ability to effectively handle imbalanced data. Physical insights were gained through model explanation with a SHapley Additive exPlanations (SHAP) value analysis, pinpointing the role of the southward magnetic field component in triggering geomagnetic storms, as well as the critical impact of shock-ICME combinations in intensifying these storms. The effective application of an SVM model with SHAP value analysis offers a way to understand and predict the geoeffectiveness of ICMEs. It also underscores the capability of a relatively simple machine learning model in predicting space weather and revealing the underlying physical mechanisms.

中文翻译：

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评估行星际日冕物质抛射的地球效应：来自支持向量机方法和 SHAP 值分析的见解


在这项研究中，我们编制了 1996 年至 2023 年 510 次行星际日冕物质抛射 (ICME) 事件的数据集，并训练了径向基函数支持向量机 (RBF-SVM) 模型，以研究 ICME 的地球有效性及其对太阳的依赖性1 天文单位观测到的风况。该模型在对特定扰动风暴时间阈值下的地磁风暴强度进行分类以及评估 ICME 的地磁效应方面表现出高性能。该模型的输出使用精确度、召回率、F1 分数和真实技能统计 (TSS) 进行评估，并通过分层 k 倍交叉验证来补充稳健性。在 -50 nT 阈值下，模型的精度分别为 0.84 和 0.93，召回率为 0.94 和 0.82，相应的 F1 分数分别为 0.89 和 0.87。总体准确率为 0.88，TSS 为 0.76。尽管由于数据集不平衡和样本有限而在 -100 nT 阈值上面临挑战，但该模型仍保持了 0.87 的总体精度，TSS 为 0.69，证明了该模型有效处理不平衡数据的能力。通过使用 SHapley 附加解释 (SHAP) 值分析的模型解释获得了物理见解，确定了南向磁场分量在触发地磁风暴中的作用，以及冲击波与 ICME 组合在加剧这些风暴中的关键影响。 SVM 模型与 SHAP 值分析的有效应用提供了一种理解和预测 ICME 地理有效性的方法。它还强调了相对简单的机器学习模型在预测太空天气和揭示潜在物理机制方面的能力。