基于CNN的深度学习在太阳风预测中的应用
CNN-Based Deep Learning in Solar Wind Forecasting
摘要：
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 nm波长的图像用于训练。太阳风的速度取自位于拉格朗日L1点的高级成分探测器（ACE）。所提出的CNN架构是从头开始设计的，用于使用四年的数据进行训练。假设太阳风在传播过程中速度恒定，对太阳风进行弹道追踪，从AIA图像中获得相应的日冕强度数据。该预报方案对2018年太阳风风速的预测精度为76.3 km \s，总体相关系数为0.57，明显优于基准模型。该模型的威胁得分约为0.46，在识别具有零误报的HSE时。
英文摘要：
This article implements a Convolutional Neural Network (CNN)-based deep learning model for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193Ȧ 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 km\s and an overall correlation coefficient of 0.57 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
