多分类支持向量机在泥石流危险性区划中的应用

以凉山州安宁河流域129个乡镇的泥石流危险性区划资料为依据,随机选取总样本数的2/3和1/2作为训练样本,建立不同数量训练样本下安宁河流域泥石流危险性区划的多分类SVM模型,进行以乡镇为单元的区域泥石流危险性评价研究。评价结果表明,SVM模型的预测精度随着训练样本数量的增加而提高;2个SVM模型对测试样本的预测准确率均高于相应的BP神经网络模型,对训练样本的回判准确率高于或接近于BP神经网络模型。因此,支持向量机方法是一种比神经网络方法具有更优精度和更强泛化性能的新机器学习方法,在泥石流危险性评价实践中具有十分广阔的应用前景和推广应用价值。

Application of Multi-classification Support Vector Machine in Regionalization of Debris Flow Hazards

Based on the debris flow data collected from 129 villages and towns in the Anning River valley of Liangshan Prefecture,two multi-classification support vector machine models were built to evaluate debris flow hazards of the villages and towns.86 samples from the villages and 65 samples from the towns were randomly selected as training samples and the remainders,as testing samples.Results show that the prediction accuracy of SVM model is improved with the increase of training samples and prediction accuracy of the two SVM models are higher than that of BP neural network models.Therefore,support vector machine method is a new machine learning method with higher precision and better generalization performance than neural network method.It has very broad application prospects and promotion and application values in the practice of debris flow hazard assessment.