基于计算机视觉与ARIMA-LSTM网络的太阳冕洞分析与预测建模
Analysis and Predictive Modeling of Solar Coronal Holes Using Computer Vision and ARIMA-LSTM Networks
AI总结：
本文利用计算机视觉检测SDO影像中的冕洞并估计面积，结合ARIMA-LSTM混合模型预测七天冕洞面积趋势，以揭示其行为模式及对空间天气的影响。
摘要：
在太空探索时代，太阳上的冕洞因其开放的磁场和增强的太阳风发射而对卫星和飞行器产生重要影响。本研究采用计算机视觉技术，利用太阳动力学观测站（SDO）的影像检测冕洞区域并估计其大小。此外，我们使用混合时间序列预测模型，特别是长短期记忆（LSTM）网络与ARIMA的组合，分析冕洞面积的变化趋势，并预测其在七个太阳区域上七天内的面积。通过研究时间序列数据，我们旨在识别冕洞行为的模式，并理解其对空间天气的潜在影响。
英文摘要：
In the era of space exploration, coronal holes on the sun play a significant role due to their impact on satellites and aircraft through their open magnetic fields and increased solar wind emissions. This study employs computer vision techniques to detect coronal hole regions and estimate their sizes using imagery from the Solar Dynamics Observatory (SDO). Additionally, we utilize hybrid time series prediction model, specifically combination of Long Short-Term Memory (LSTM) networks and ARIMA, to analyze trends in the area of coronal holes and predict their areas across various solar regions over a span of seven days. By examining time series data, we aim to identify patterns in coronal hole behavior and understand their potential effects on space weather.
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。
