SVM in the Sand-Dust Storm Forecasting

A novel method of the support vector machine (SVM) is proposed in the sand-dust storm-forecasting model. The development of the model includes pre-treating original data by using principal component analysis (PCA), choosing a kernel function (i.e. the radial basic function (RBF) kernel), defining the search region of (C, sigma² ) by analyzing the influence on SVM classifier of the regularization parameter and the kernel parameter, and optimizing the two parameters (C, sigma²) by using grid search in the search region. The result of the experiment shows that this SVM method has better performances than the improved back-propagation neural network (BPNN) method in terms of stability, correct classification and the running speed