Comparison of neural network and support vector machine methods for Kp forecasting

Abstract

We have made a comparison of near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. For this, we consider four models as follows: (1) a NN model using solar wind data, (2) a SVM model using solar wind data, (3) a NN model using solar wind data and preliminary Kp values from ground-based magnetometers, and (4) a SVM model using the same input data used in model 3. For the comparison, we estimate the correlation coefficients and the RMS errors, and the mean absolute errors between the observed Kp and the predicted one. As a result, we find that model 3 shows the best performance. The correlation coefficients, the RMS error, and the mean absolute error of model 3 are 0.93, 0.48, and 0.61, respectively. For the forecast evaluation of high magnetic activity occurrence for the four models, we present contingency tables and their statistical parameters such as probability of detection yes, false alarm ratio, bias, critical success index, and true skill statistic. From a comparison of these statistical parameters, we find that model 3 is superior to the other models for the forecasting of high magnetic activities (Kp ≥ 6). Key Points Kp forecasting model based on neural network and support vector machine methods ©2013. American Geophysical Union. All Rights Reserved.