[P-EM09] Space Weather and Space Climate

[PEM09-P11] 放射線帯外帯電子変動予測モデルの開発とXAIによるモデル解釈

*西宮 祐太¹、三好 由純¹、堀 智昭¹、中村 紗都子¹、小路 真史¹、Jun Chae-Woo¹、三谷 烈史²、篠原 育²、浅村 和史²、東尾 奈々²、齊藤 慎司³、塩田 大幸³ (1.名古屋大学宇宙地球環境研究所、2.宇宙航空研究開発機構宇宙科学研究所、3.情報通信研究機構)

The radiation belt is the region in the inner magnetosphere where the most energetic electrons are trapped by the Earth's magnetic field. The spatial and temporal variations of the electron flux are large particularly in the outer radiation belt, and a sustained large flux of the outer belt electrons may lead to satellite anomaly. The prediction of energetic electron flux variation is therefore of significant importance in mitigating these risks. We have developed an outer radiation belt electron forecast model that utilizes Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) techniques. This model is designed to forecast the time-varying electron flux in the outer radiation belt at energies ranging from several hundred keV to several MeV at L = 4-6, based on input data, such as observed solar wind velocity, IMF, and the outer radiation belt electron flux for last three days. The model succeeds in predicting the electron flux in most case; however, it tends to give less accurate flux values when the flux decreases at high L positions. To address this issue, we have improved the model by incorporating additional solar wind parameters. Previous research has indicated that the solar wind dynamic pressure contributes to the loss of energetic electrons via magnetopause shadowing, and thus including this parameter as an input may enhance the forecast accuracy. Our revised model that incorporates the solar wind dynamic pressure as an input demonstrates improved prediction accuracy. Moreover, we have also incorporated Explainable Artificial Intelligence (XAI) into the model to investigate the impact of each input parameter on the electron flux.