Support vector machine prediction model based on slope displacement monitoring data

Based on the statistical learning theory and the principle of the minimum structural risk,the support vector machine(SVM) method has the excellent extrapolating ability for regression prediction and good applicability to the problem on small training data.The extrapolating ability and predicting capability have been validated by comparing the monitoring values and predicted ones obtained by using the support vector machine prediction model built on displacement monitoring data of the slope project.Based on the new data obtained by generating operation on the initial monitoring data of the slope project,the predicted results are figured out with SVM model correspondingly and good prediction precision is approved as well by comparing the predicted results based on the initial data and new data.The influence of the selected training data on the prediction precision is also analyzed.The sensitivity analysis of the parameters of SVM model is made as well.Moreover,the precision of prediction is improved by using one of evolutionary algorithms,particle swarm optimization algorithms,to optimize the key parameters of SVM model.