摘要
以利用气象数据预测森林火险等级为目的,使用半正定规划建模选定支持向量机的最优核函数,依据500余条林火数据建立了支持向量回归机模型,并使用回归误差特征曲线图对比分析各个回归模型的学习效果。分析得到该自定义核函数的均方误差为1.76,支持向量数为190,约占训练数据集的1/2。结果表明,与传统的线性回归方法及基于高斯核的支持向量机相比,该预测模型具有较高的准确率并且有效的避免了过学习的现象。
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.
出处
《中国农学通报》
CSCD
2012年第13期126-131,共6页
Chinese Agricultural Science Bulletin
关键词
森林火险预测
半定规划
支持向量回归机
回归误差特征曲线图(REC)
forest fire prediction
semi-definite programming （SDP）
support vector machine （SVM）
regression error characteristic curves （REC）
