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摘要: 利用神经网络技术并考虑地磁活动的周期性, 提出了一种提前一小时预报Dst指数的方法. 网络的输入包括时间、季节、当前时刻及其一阶增量、二阶增量、前27天Dst指数的平均值. 以下一时刻Dst指数作为输出对网络进行训练, 训练好的网络可以提前一小时预报Dst指数. 分别用1985年、1986年、1990年和1991年Dst指数数据进行检验. 结果表明, 预报结果与观测数据符合较好, Dst指数预报误差的均方根分别为4.00 nT, 3.72 nT, 5.35 nT, 6.82 nT. 误差分析表明, Dst 指数的预报结果太阳活动低年比高年好.Abstract: Magnetic storm has always been one of the key issues of solar-terrestrial space physics for the past half century. The Dst index is the longitudinally averaged magnetic field depression at low latitudes. It is the primary measure of the magnitude of magnetic storms, and provides a convenient way to monitor the magnetospheric ring current. By using artificial neural network and considering the effects of the period of the geomagnetic activity, this paper brought out a method of forecasting Dst index, an hour in advance. The inputs of the network include time, season, Dst index and the first difference and the second difference of Dst index, the mean value of 27 days ago at t time, and the output is the observed Dst index data at next time. The trained net then can forecast Dst index 1 h ahead. Some examples are presented by using the Dst index data in 1985, 1986, 1990, 1991, respectively. The results indicate that the predicted Dst index has good agreement with observed data and the corresponding root mean errors of the model comparing with the measurement were 4.00 nT, 3.72 nT, 5.35 nT and 6.82 nT, respectively. In addition, the error analysis indicates that the predicted root-mean-square error of Dst index is smaller in low solar activities than in high solar activities.
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Key words:
- Dst index /
- Neural network /
- Short-term forecast
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