講演情報
[PEM10-P05]Development of Classification and Regression Models for SEP Events Forecasting with Machine/Deep Learning Methods
*加藤 裕太1,2、草野 完也2、藤田 菜穂1、土井 健太郎1、津田 明1、川崎 裕之1、森山 智生2、久保 友樹3、光田 千紘1,2 (1.富士通株式会社、2.名古屋大学 宇宙地球環境研究所、3.新潟大学大学院 自然科学科学研究科 電気情報工学専攻)
キーワード：
太陽高エネルギー粒子、説明可能AI、機械学習、深層学習、時系列予測
Fujitsu and the Institute for Space–Earth Environmental Research (ISEE), Nagoya University, have been conducting joint research since September 2023 to establish space weather forecasts for the safety of human activities in space and on the Moon.
Solar Energetic Particle (SEP) events, which occur in conjunction with solar flares and coronal mass ejections, are significant space weather phenomena known to impact human health, spacecraft, and ground-based systems.
This presentation includes the current results and ongoing status as follows.
(1) SEP event occurrence prediction using a classification task with explainable AI (WideLeaning)
(2) SEP event particle flux, fluence, and time-series forecasting using a regression task with the machine and deep learning methods
We discuss possible SEP event prediction schemes with these developed models and the future prospects of this industry-academia collaboration.
Solar Energetic Particle (SEP) events, which occur in conjunction with solar flares and coronal mass ejections, are significant space weather phenomena known to impact human health, spacecraft, and ground-based systems.
This presentation includes the current results and ongoing status as follows.
(1) SEP event occurrence prediction using a classification task with explainable AI (WideLeaning)
(2) SEP event particle flux, fluence, and time-series forecasting using a regression task with the machine and deep learning methods
We discuss possible SEP event prediction schemes with these developed models and the future prospects of this industry-academia collaboration.