基于深度学习递归神经网络的电离层总电子含量经验预报模型

利用行星际太阳风参数与太阳活动指数、地磁活动指数、电离层总电子含量格点化地图数据，首次基于一种能处理时间序列的深度学习递归神经网络（Recurrent Neural Network，RNN），建立提前24h的单站电离层TEC预报模型.对北京站（40°N，115°E）的预测结果显示，RNN对扰动电离层的预测误差低于反向传播神经网络（Back Propagation Neural Network，BPNN）0.49～1.46TECU，将太阳风参数加入预报因子模型后对电离层正暴预测准确率的提升可达16.8%.RNN对2001和2015年31个强电离层暴预报的均方根误差比BPNN低0.2TECU，将太阳风参数加入RNN模型可使31个事件的平均预报误差降低0.36～0.47TECU.研究结果表明深度递归神经网络比BPNN更适用于电离层TEC的短期预报，且在预报因子中加入太阳风数据对电离层正暴的预报效果有明显改善. 

Prediction Model for Ionospheric Total Electron Content Based on Deep Learning Recurrent Neural Networkormalsize

A 24h ahead forecasting model for ionospheric Total Electron Content (TEC) at Beijing station is established based on the deep learning Recurrent Neural Network (RNN) for the first time. The model implementation requires solar 10.7cm flux index, geomagnetic index ap, grid map of TEC, solar wind speed and the southward components of interplanetary magnetic field. The predicting results for Beijing station (40°N, 115°E) show that the Root Mean Square Error (RMSE) of the disturbed ionosphere TEC predicted by RNN model is lower than that of BPNN (Back Propagation Neural Network) model by 0.49~1.46TECU. The forecasting accuracy of ionospheric positive storm by RNN model is increased by 16.8% with solar wind parameters. Furthermore, the RMSE of RNN model of 31 strong TEC storms in 2001 and 2015 are less than those of BPNN model by 0.2TECU, and the RMSE of RNN model is decreased by 0.36~0.47TECU as solar wind parameters are added. The results indicate that RNN model is more reliable than BP model for short-term forecasting of TEC. Moreover, the addition of interplanetary solar wind parameters are helpful for predicting TEC positive storm.