提高支持向量机洪水峰值预报精度研究

Study on improving peak flood forecast accuracy with SVM model

本文引入支持向量机算法进行流域洪水预报建模 ,同时针对训练样本的不平衡性 ,提出了一种能进行峰值识别的改进支持向量机算法 (SupportVectorMachinewithPeakRecognizer ,简称SVMPR)。该算法在结构风险最小化准则的目标函数中适当加大峰值样本的权重 ,从而提高支持向量机洪水预报模型对洪峰的预报精度。分别采用SVM算法和SVMPR算法对沙溪口流域上洋口站建立洪水预报模型 ,对比分析表明了SVMPR算法的有效性。

In this paper, support vector machine (SVM) theory is introduced for flood forecast model. In response to the imbalance of training samples, a modified SVM algorithm with peak recognition theory(SVMPR) is proposed. In this algorithm, weight to the peak samples is properly increased to objective function of structural risk minimization. Consequently, the accuracy of flood peak forecast is greatly improved. Flood forecast model was established for Yangkou station in Saxikou basin by SVM and SVMPR algorithm. The contrastive analysis showed the validity of SVMPR algorithm.