Electron density irregularities present within the ionosphere induce significant fluctuations in global navigation satellite system (GNSS) signals. Fluctuations in signal power are referred to as amplitude scintillation and can be monitored through the S4 index. Forecasting the severity of amplitude scintillation based on historical S4 index data is beneficial when real-time data is unavailable. In this work, we study the possibility of using historical data from a single GPS scintillation monitoring receiver to train a machine learning (ML) model to forecast the severity of amplitude scintillation, either weak, moderate, or severe, with respect to temporal and spatial parameters. Six different ML models were evaluated and the bagged trees model was the most accurate among them, achieving a forecasting accuracy of $81\%$ using a balanced dataset, and $97\%$ using an imbalanced dataset.

翻译：电离层内存在的电子密度不规则现象引起全球导航卫星系统信号的大幅波动,信号功率的波动被称为振动闪烁,可通过S4指数监测。根据历史S4指数数据预测振动闪烁的强度,在没有实时数据时是有益的。在这项工作中,我们研究是否有可能利用单个全球定位系统闪烁监测接收器的历史数据来训练一个机器学习模型,以预测振荡的强度,无论是微弱的、中度的还是严重的时间和空间参数。对六种不同的ML模型进行了评估,包装树木模型是最准确的,利用平衡数据集预测准确度为81美元,使用不平衡数据集预测准确度为97美元。