首页|基于神经网络的未来3天Kp指数预报建模与可解释AI应用

基于神经网络的未来3天Kp指数预报建模与可解释AI应用

Modeling Next 3-day Kp Index Forecasting with Neural Networks and Exploring the Application of Explainable AI

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
当前业务中对未来 3天Kp指数预报需求强烈.但地磁暴中多参数耦合导致难以量化各预报因子对Kp值的贡献,制约了预报精度提升.本文构建了神经网络 3天Kp指数预报模型,并使用人工智能(AI)可解释性算法定量化各因子贡献.结果显示,行星际磁场南向分量在提前 3h对Kp指数的贡献为 37.15%,为主要因子,说明模型能捕捉符合物理特征的主要预报因子.Kp指数历史特征贡献随提前量逐渐增加,提前 3天总体贡献占68.06%,验证了对冕洞高速流引起的地磁暴事件的预报能力.对 2015和 2017年特大地磁暴进行贡献分析,模型准确捕捉了地磁暴多参数耦合的复杂特性.研究表明,可解释AI算法在一定程度上能定量化各预报因子对Kp指数的预报贡献,有助于改进未来3天Kp指数AI预报模型.
The current operational needs of space weather forecasting strongly require accurate predic-tions of the future 3-day Kp index.Such forecasts involve a multitude of predictors,including physical parameters observed at the Earth-Sun L1 point and historical characteristics of the Kp index.Therefore,previous research primarily relied on statistical or empirical methods for prediction.However,the com-plex coupling of multiple parameters during geomagnetic storm events has made it challenging to quanti-fy the contributions of various predictors to Kp index forecasting over a 3-day timescale,hindering fur-ther improvements in forecast accuracy.This study builds a 3-day Kp index forecasting model based on neural network modeling and utilizes explainable AI(Artificial Intelligence)algorithm,specifically the in-tegrated gradient algorithm,to quantify the contributions of individual predictor.The research results indicate that the southward interplanetary magnetic field contributes significantly to Kp index predic-tion,accounting for 37.15%of all factors,making it the primary contributor.Following this,solar wind speed contributes 15.73%,underscoring the model's ability to capture parameters aligned with physical characteristics as the primary predictive factors during training.The contribution of historical character-istics of Kp index(recurrence characteristics)gradually increases with the forecasting horizon and reach-es 68.06%at a lead time of 3-day.This substantiates the strong predictive capabilities of the AI model in forecasting geomagnetic storm events induced by high-speed solar wind streams originating from coronal holes.Furthermore,this study conducts contribution analysis on two significant geomagnetic storm events that occurred in 2015 and 2017.It reveals that the predominant predictors contributing to each event differ.This underscores the model's capability to accurately capture the complex coupling of multi-ple parameters in geomagnetic storm forecasting.In conclusion,this research demonstrates that employ-ing explainable AI algorithms can help quantify the contributions of various predictive factors to Kp in-dex forecasting to some extent.This has the potential to enhance further research and improvements in 3-day Kp index AI forecasting models.

Geomagnetic storm3-day Kp index forecastingNeural networkExplainable AI algorithm

王听雨、罗冰显、陈艳红、石育榕、王晶晶、刘四清

展开 >

中国科学院国家空间科学中心空间天气学国家重点实验室 北京 100190

中国科学院大学 北京 100049

地磁暴 未来3天Kp指数预报 神经网络 可解释性AI算法

中国科学院战略性先导科技专项国家自然科学基金面上项目中国科学院重点部署项目中国科学院国家空间科学中心"攀登计划"青年创新课题中国科学院青年创新促进会共同资助

XDB056000042074224ZDRE-KT-2021-3E4PD40012S

2024

10.11728/cjss2024.03.2023-0107

空间科学学报
中国科学院空间科学与应用研究中心 中国空间科学学会

空间科学学报

CSTPCD北大核心
影响因子：0.328
ISSN：0254-6124
年,卷(期)：2024.44(3)
  • 4