Application of Support Vector Machine to the Forecasting of Dst Index during Geomagnetic Storm
In this study the support vector machine is applied to the forecasting of Dst index during intense geomagnetic storms (Dst ≤-100nT) that occurred from 1995 to 2014.We collect 2662 Dst indices and use the corresponding solar wind data as model input.We also build Neural Network and Linear machine as comparison,and improve the reliability of the predicted results by using K-fold cross validation.For comparison,we calculate the Correlation Coefficient (CC),the RMS errors,the Mean Absolute Error of the minimum Dst (Em) and the Mean Absolute Error of the time when the minimum Dst occurred (Et) between the observed Dst data and the predicted one.As a result,we find that SVM shows the best prediction performance for all events:CC is 0.89,RMS is 24.27nT,Em is 17.35nT and Et is 3.2 hours respectively.For further comparison,the 80 storm events are divided into two groups depending on the minimum value of Dst index.It is shown that the forecasting performance of SVM is better than other models both in the intense (-200 ＜ Dstmin ≤-100nT)and the super intense geomagnetic storm (Dstmin ≤-200 nT) groups.
Support Vector Machine (SVM)ForecastingGeomagnetic stormDst index
国家科技期刊平台
NSTL
万方数据
