日本地球惑星科学連合2018年大会

講演情報

[JJ] Eveningポスター発表

セッション記号 M (領域外・複数領域) » M-IS ジョイント

[M-IS09] 地震・火山等の地殻活動に伴う地圏・大気圏・電離圏電磁現象

2018年5月22日(火) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:児玉 哲哉(宇宙航空研究開発機構研究開発部門第一研究ユニット)、長尾 年恭(東海大学海洋研究所)、芳原 容英(電気通信大学 大学院情報理工学研究科)

[MIS09-P06] Nonlinear prediction model of ionospheric foF2 variability

*Hendy Santosa1,2Hobara Yasuhide1,3,4 (1.Department of Communication Engineering and Informatics, The University of Electro-Communications, Tokyo, Japan、2.Department of Electrical Engineering, Bengkulu University, Bengkulu, Indonesia、3.Center for Space Science and Radio Engineering, The University of Electro-Communications, Tokyo, Japan、4.Earth Environment Research Station, The University of Electro-Communications, Tokyo, Japan)

キーワード:nonlinear autoregressive with exogenous input neural network、one step ahead prediction、foF2

Nonlinear autoregressive with exogenous input neural network (NARXNN) for one step ahead prediction of the F2 layer critical frequency (foF2) at Kokubunji ionosonde station (35.72° N, 139.49° E) was developed. The daily or hourly variation of foF2 is an output, and solar activity (DOY1, DOY2, SSN, F10.7 index) and magnetic activity (Dst, AE, and Kp indices) parameters are the exogenous inputs with a time interval from 1 January 1964 to 31 December 2016. The performance of NARXNN model was evaluated by using the Pearson’s correlation coefficient (r) and root mean square error (RMSE). The results show that predicted values of foF2 have very good agreement with observed values. Moreover, the constructed model has a high Pearson’s correlation coefficient and a small RMSE. Therefore the constructed model based on the proposed methodology provides a good prediction of foF2 for Kokubunji ionosonde station, and solar flux at 10.7 cm is recognized as the most significant external forcings contributing to foF2 prediction.