Abstract:

The support vector machine (SVM) was employed to predict the burned area of forest fires, to build the optimal kernel, and the semi-definite programming was used when solving the SVM problem. Also, the regression error characteristic curves were provided to illustrate the accuracy difference between the classic regression model as well as the SVM model based on gauss kernel. The experience had the result of 1.76 of the mean square error and 190 of the support vector number, approximately a half of the training number, which showed that the model had a relatively high accuracy compared to the classic regression method and a prevailing kernel. The result also indicated that the method effectively avoided the over-learning phenomenon and that it was useful for improving firefighting resource management.