利用行星际监测数据进行地磁暴预报

利用全连接神经网络方法应用于地磁Dst指数的预报中．对ACE卫星探测的太阳风和行星际磁场及其变化对未来几小时的Dst指数的影响进行了统计分析,发现在这些行星际实测参数中,对Dst指数作用较为明显的是太阳风速度、太阳风质子密度和行星际磁场南向分量,同时,当前Dst指数实测值对今后几小时的Dst 指数已有很强的制约作用．在统计分析的基础上,建立了全连接神经网络预报模型．由于采用了全连接神经网络结构,模式能够反映出太阳风、行星际磁场等参数与地磁Dst指数参数的复杂联系,可以自动建立输入参量的最佳组合方式,提高了预报精度．通过利用大量实测数据对神经网络模式进行训练,最终建立了利用优选的ACE 卫星行星际监测数据提前2 h对Dst指数进行预报．通过检测,预报的误差为14．3％．

Forecasting Dst Index With Artificial Neural Network

In this work, fully connected neural network, a new kind of artificial network, has been introduced to construct the model for Dst index forecasting, Through studying the mechanism that the geomagnetosphere was affected by the condition of interplanetary media, the geomagnetic disturbance index Dst was found to have close, and complex relationship with both the solar wind parameters and IMF features. By employing the measured parameters from ACE spacecraft, these parameters were the solar wind velocity, the density of solar wind plasma and the southward component of IMF. The most recent measured Dst was also figured to correlate to the Dst several hours ahead. To construct the relationship between interplanetary measured parameters and Dst index, fully connected neural network was introduced. This neural network could demonstrate the complex relationship through building up the internal connection between separate neurons in hidden layer. After a training process with historical data, the forecast model was built during which the neural network will adjust the internal connect weight between units automatically according to the input parameters. The storm time data of 1998 and 1999 was selected in the training process of model construction. The data set during the geomagnetic storm in July 24-29 was used to test the model and the error of the test data was 14.3%.