从大坝变形监测资料入手,针对传统统计方法多适用监测资料长序列的情况,充分利用支持向量机在解决小样本、非线性问题中的优势,建立了基于支持向量机的大坝变形监控模型,通过对某混凝土坝变形监测资料的分析和计算,并与传统统计模型、BP神经网络模型结果进行比较,证明了支持向量机模型在拟合和预测方面均具有更高的精度和良好的泛化能力。 Starting from the monitoring data of dam deformation, aiming at the long application of traditional statistical methods to monitor the long sequence of data, this paper makes full use of the advantages of support vector machines in solving small samples and nonlinear problems, and establishes a dam deformation monitoring model based on support vector machines Through the analysis and calculation of the deformation monitoring data of a concrete dam and the comparison with the traditional statistical model and the BP neural network model results, it is proved that the support vector machine model has higher accuracy and good generalization in the fitting and prediction Ability.