| Project/Area Number |
15K17620
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Astronomy
|
|---|
| Research Institution | National Institute of Information and Communications Technology |
|---|
Principal Investigator |
Nishizuka Naoto 国立研究開発法人情報通信研究機構, 電磁波研究所宇宙環境研究室, 研究員 (10578933)
|
|---|
| Project Period (FY) |
2015-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
|
|---|
| Keywords | 太陽フレア / 予測 / 宇宙天気 / 機械学習 / 衛星画像 / 画像検出 / 深層学習 / ビッグデータ / ベクトル磁場 / SDO衛星 / フレア予測 / 宇宙天気予報 / ビッグデータ解析 |
|---|
| Outline of Final Research Achievements |
Our flare prediction model using machine-learning techniques and big data of solar observation images dramatically improved our skill of the space weather forecast. Whereas the space weather forecasting service has been delivered every day for a long time, we had a difficulty in improving its accuracy. In this research, we applied machine-learning techniques to solar observation data analysis, and we developed a new model to predict solar flares statistically by dealing with the huge amount of data. As a result, we succeeded in improving the prediction accuracy up to 80 %, much better than the human forecasting around 50 %. We also revealed the ranking of features effective for flare prediction. Using our new prediction model, the real-time forecasting operation system of solar flares will be prepared soon.
|
