An intelligent solar flare prediction model based on X-ray flux curves using Long Short-Term Memory  

作　　者：Yan Gao Li Zhang Long Xu 

机构地区：[1]College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China [2]State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China [3]Faculty of Electrical Engineering and Computer Science,Ningbo University,Ningbo 315211,China

出　　处：《Astronomical Techniques and Instruments》2025年第2期65-72,共8页天文技术与仪器(英文)

基　　金：partially supported by the National Key R&D Program of China (2022YFE0133700);the National Natural Science Foundation of China(12273007);the Guizhou Provincial Excellent Young Science and Technology Talent Program (YQK[2023]006);the National SKA Program of China (2020SKA0110300);the National Natural Science Foundation of China(11963003);the Guizhou Provincial Basic Research Program (Natural Science)(ZK[2022]143);the Cultivation project of Guizhou University ([2020]76).

摘　　要：Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic storms.Consequently,it is very important to accurately predict the time period of solar flares.This paper proposes a flare prediction model,based on physical images of active solar regions.We employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 hours.The reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 hours.The True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and 0.5556.Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.

关 键 词：Neural Network Long Short-Term Memory Solar flare prediction X-ray flux curve 

分 类 号：P182.52[天文地球—天文学]