空间科学学报2025，Vol.45Issue(3)：641-652,12.DOI:10.11728/cjss2025.03.2024-0045
基于LSTM神经网络的Dst指数预报方法
Dst Index Prediction Method Based on LSTM Neural Network
摘要
Abstract
The Dst index is one of the widely used hourly geomagnetic indices to reflect geomagnetic storm processes,and forecasting the Dst index constitutes a primary concern in modern space weather studies.This study leverages Long Short-Term Memory(LSTM)neural network methodology alongside solar wind parameters and Dst index data spanning from 2008 to 2022 to construct a predictive model for the Dst index.Two models are established:the LSTM model,modeling the entire temporal domain,and the Storm model,modeling solely data from storm periods.Employing the LSTM model for rolling forecasts of Dst index during 2001 to 2002 yields a correlation coefficient exceeding 0.94 and a root mean square error within 11 nT for forecasts ranging from 1 to 6 hours in advance.The Storm model effective-ly addresses the issue of pronounced forecast errors during storm periods,particularly during the main phase of intense storms(Dst<-100 nT),showcasing improved forecast accuracy.Forecasting experi-ments conducted on 23 strong storm events occurring during 2001―2002 demonstrate an enhancement in the correlation coefficient for forecasts made 6 hours in advance during storm periods,increasing from 0.902 with the LSTM model to 0.948 with the Storm model.Integration of both forecasting models into the LSTM-Storm model yields correlation coefficients above 0.95 and root mean square errors within 9 nT for Dst index forecasts,presenting a marked improvement in forecasting accuracy compared to the standalone LSTM model.关键词
Dst指数预报/LSTM神经网络/预报模型/LSTM-Storm模型Key words
Dst index prediction/LSTM neural network/Forecasting model/LSTM-Storm model分类
天文与地球科学引用本文复制引用
李绍文,牛俊,梅冰,姚俐竹,李炎斌..基于LSTM神经网络的Dst指数预报方法[J].空间科学学报,2025,45(3):641-652,12.基金项目
国家重点研发计划项目资助(2023YFC2808904) （2023YFC2808904）
