Forecasting the Timing of Peak Solar Flare Index in Solar Cycle 25 for Improved Space Weather Prediction

被引:0
|
作者
He, Mu [1 ]
Zhu, Hongbing [2 ]
机构
[1] Suzhou Chien Shiung Inst Technol, Coll Artificial Intelligence, Suzhou 215411, Peoples R China
[2] Avant Courier Lab, Hiroshima 7360067, Japan
关键词
LSTM plus; Solar Cycle 25; Solar Flare Index; space weather prediction; sunspot numbers;
D O I
10.1155/aa/7366830
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Solar flares, driven by magnetic reconnection on the Sun, pose substantial threats to Earth's technological infrastructure. Accurate forecasting of flare activity, quantified by the Solar Flare Index (SFI), is crucial for mitigating space weather risks. This study leveraged an optimized long short-term memory (LSTM+) neural network, to achieve robust long-term SFI predictions. The LSTM + model incorporated a novel reprediction procedure and fine-tuned parameter optimization, demonstrating high accuracy in hindcasting SFI for Solar Cycles 23 and 24. The validated model predicted a peak SFI for Solar Cycle 25 in January 2025, aligning with historical trends of SFI lagging behind sunspot number maxima. This projection, along with the recent resurgence in sunspot activity, suggests a potential second, higher SFI peak may occur. Incorporating inherent model uncertainties, the maximum SFI for Solar Cycle 25 was estimated to occur between December 2023 and February 2026. These findings contribute to a deeper understanding of solar flare dynamics and provide valuable insights for space weather prediction, enabling proactive measures to protect critical technological systems.
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页数:7
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