Abstract:A SVM model is established for predicting dam deformation, and optimizing the kernel function parameter, penalty parameter and loss function parameter through the genetic algorithm. We use this model to analyze the long period deformation monitoring data and make predications. In this paper, we compare horizontally different kernel functions of support vector machine using the same optimization method, and the same kernel function of support vector machine using different optimization methods. The results show that GA-SVM(RBF) not only can well predict the dam deformation trend, but also improves the prediction accuracy over contrasting BP neural networks, AR(p), multiple regression analysis and periodic function fitting longitudinally.
SHEN Zhehui,HUANG Teng,SHEN Yueqian et al. Dam Deformation Monitoring Prediction on Support Vector Machine Optimized by Genetic Algorithm[J]. jgg, 2016, 36(10): 927-930.