This article implements a Convolutional Neural Network (CNN)-based deep-learning model for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193 Å wavelength are used for training. Solar-wind speed is taken from the Advanced Composition Explorer (ACE) located at the Lagrangian L₁ point. The proposed CNN architecture is designed from scratch for training with four years’ data. The solar-wind has been ballistically traced back to the Sun assuming a constant speed during propagation, to obtain the corresponding coronal-intensity data from AIA images. This forecasting scheme can predict the solar-wind speed well with a RMSE of 76.3 ± 1.87 km s⁻¹ and an overall correlation coefficient of 0.57 ± 0.02 for the year 2018, while significantly outperforming benchmark models. The threat score for the model is around 0.46 in identifying the HSEs with zero false alarms.

本文实现了一个基于卷积神经网络 (CNN) 的深度学习模型，用于太阳风预测。来自大气成像组件(AIA) 的 193 Å 波长图像用于训练。太阳风速取自位于拉格朗日 L ₁点的高级成分浏览器(ACE) 。所提出的 CNN 架构是从头开始设计的，用于训练四年的数据。假设在传播过程中保持恒定速度，太阳风已被弹道追溯到太阳，以从 AIA 图像中获得相应的日冕强度数据。该预测方案可以很好地预测太阳风速，RMSE 为 76.3 ± 1.87 km s ^-12018 年的整体相关系数为 0.57±0.02，同时显着优于基准模型。在识别具有零误报的 HSE 时，该模型的威胁评分约为 0.46。