A machine learning-based model for the next 3-day geomagnetic index (Kp) forecast - Wang, Jingjing; Luo, Bingxian; Liu, Siqing; Shi, Liqin (Frontiers in Astronomy and Space Sciences, 2023)
The 3-day Kp forecast product is important and necessary for space weather forecasts. There is some essential information that can be obtained from the 3-day Kp forecast product, such as the start time of the geomagnetic storm, the maximum storm level, and the storm duration. In this study, we aimed to predict the next 3-day Kp index based on the previous Kp time series and SDO/AIA 193 Å images. We prepared datasets from May 2010 to December 2019 for training and datasets from January 2020 to October 2022 for testing. The similarity parameters of the previous and current geomagnetic conditions between the samples are calculated and analyzed. We assumed that the paired samples with high-similarity parameters of the previous and current geomagnetic conditions would also have high-similarity parameters of the next 3-day geomagnetic conditions. Based on the assumption, we selected the three best similarity parameters through the feature selection process and adopted the scalable tree boosting system (XGBoost) to develop a prediction model. It took the similarity parameters of the previous and current geomagnetic conditions as input and provided the best match sample from the training subset as a forecast. For the next 3-day non-storm (maximum Kp < 5) prediction period, our model reached an F1-score of 0.96. For the next 3-day storm (maximum Kp ≥ 5) prediction period, our model reached an F1-score of 0.82, a recall of 0.70, and a precision of 0.98.