磁场参数和时间步长对太阳耀斑预测深度学习模型的影响

IF 1.8 4区 物理与天体物理 Q3 ASTRONOMY & ASTROPHYSICS
Jinfang Wei, Yanfang Zheng, Xuebao Li, Changtian Xiang, Pengchao Yan, Xusheng Huang, Liang Dong, Hengrui Lou, Shuainan Yan, Hongwei Ye, Xuefeng Li, Shunhuang Zhang, Yexin Pan, Huiwen Wu
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引用次数: 0

摘要

太阳耀斑预测研究对保障人类活动具有重要的实用价值和科学价值。目前的太阳耀斑预测模型没有充分考虑时间步长等重要因素,也没有对多个模型的物理特征进行对比分析,更没有探讨特征重要性的一致性。在这项工作中,我们基于SDO的SHARP数据,建立了9个基于机器学习的太阳耀斑预测模型,对未来24小时内的太阳耀斑进行二元 "是 "或 "否 "类预测,并研究了不同时间步长和其他因素对预测性能的影响。主要结果如下(1)随着时间步长的增加,8个深度学习模型的预测性能呈上升趋势,在时间步长为40时,模型的预测性能最好。 2)在预测≥C类和≥M类太阳耀斑时,深度学习模型的真技能统计量(TSS)一直优于基线模型。对于同一模型,预测≥M 级耀斑的 TSS 通常超过预测≥C 级耀斑的 TSS。(3) 深度学习模型在预测≥C 级耀斑时的 Brier Skill Score(BSS)明显超过基线模型。然而,在预测≥M 级耀斑时,九个模型的 BSS 分数不相上下。对于同一模型,预测≥C 级耀斑的 BSS 值普遍高于预测≥M 级耀斑的 BSS 值。(4) 通过对多个模型的特征重要性分析,确定了排名始终靠前和靠后的共同特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The influence of magnetic field parameters and time step on deep learning models of solar flare prediction

The influence of magnetic field parameters and time step on deep learning models of solar flare prediction

The research on solar flare predicting holds significant practical and scientific value for safeguarding human activities. Current solar flare prediction models have not fully considered important factors such as time step length, nor have they conducted a comparative analysis of the physical features in multiple models or explored the consistency in the importance of features. In this work, based on SHARP data from SDO, we build 9 machine learning-based solar flare prediction models for binary “Yes” or “No” class prediction within the next 24 hours, and study the impact of different time steps and other factors on the forecasting performance. The main results are as follows. (1) The predictive performance of eight deep learning models shows an increasing trend as the time step length increases, and the models perform the best at the length of 40. (2) In predicting solar flares of ≥C class and ≥M class, the True Skill Statistic(TSS) of deep learning models consistently outperforms that of baseline model. For the same model, the TSS for predicting ≥M class flares generally exceeds that for predicting ≥C class flares. (3) The Brier Skill Score (BSS) of deep learning models significantly surpasses that of baseline model in predicting ≥C class flares. However, the BSS scores of the nine models are comparable for predicting ≥M class flares. For the same model, the BSS for predicting ≥C class flares is generally higher than that for predicting ≥M class flares. (4) Through feature importance analysis of multiple models, the common features that consistently rank at the top and bottom are identified.

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来源期刊
Astrophysics and Space Science
Astrophysics and Space Science 地学天文-天文与天体物理
CiteScore
3.40
自引率
5.30%
发文量
106
审稿时长
2-4 weeks
期刊介绍: Astrophysics and Space Science publishes original contributions and invited reviews covering the entire range of astronomy, astrophysics, astrophysical cosmology, planetary and space science and the astrophysical aspects of astrobiology. This includes both observational and theoretical research, the techniques of astronomical instrumentation and data analysis and astronomical space instrumentation. We particularly welcome papers in the general fields of high-energy astrophysics, astrophysical and astrochemical studies of the interstellar medium including star formation, planetary astrophysics, the formation and evolution of galaxies and the evolution of large scale structure in the Universe. Papers in mathematical physics or in general relativity which do not establish clear astrophysical applications will no longer be considered. The journal also publishes topically selected special issues in research fields of particular scientific interest. These consist of both invited reviews and original research papers. Conference proceedings will not be considered. All papers published in the journal are subject to thorough and strict peer-reviewing. Astrophysics and Space Science features short publication times after acceptance and colour printing free of charge.
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