基于SVM的混凝土坝变形监控模型预测能力实例分析

大坝变形与水位、温度、时效等较多因素非线性相关,支持向量机(Support Vector Machine, SVM)适用于小样本、非线性、高维学习问题,在大坝安全变形监控上具有很大的优越性。阐述了支持向量机的原理,介绍了应用SVM建立混凝土坝变形监控模型的步骤及其参数优化方法。针对预测样本数目的合理取值问题,通过实例分析,研究基于SVM的混凝土坝变形监控模型的预测能力。结果表明,基于SVM的混凝土坝变形监控模型短期预测能力优于长期预测能力,且其预测能力受预测集数目的影响大于算法优化的影响。因此,合理选择预测集数目对变形监控模型有效预测尤为重要。

Case Analysis of the Prediction Ability of SVM-based Monitoring Model for Concrete Dam Deformation

Dam deformation is nonlinearly correlated with water level, temperature, aging and many other factors. Support vector machine (SVM) is of great superiority in dam safety monitoring as it accommodates small sample, nonlinear and high dimensional learning problems. In this article, the principle of SVM is expounded, the procedures of building an SVM-based deformation monitoring model are summarized, and parameter optimization method is introduced as well. The prediction ability of the SVM-based monitoring model for concrete dam deformation is analyzed through a case study. Results demonstrate that the short term prediction ability of the model is better than its long term prediction ability; the prediction ability is affected by the number of prediction sets rather than by algorithm optimization. The results indicate that selecting an appropriate number of prediction sets is important to the validity of the model.