The nature of statistical learning theory
The nature of statistical learning theory
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
The Journal of Machine Learning Research
Multi-output Support Vector Machine Regression and Its Online Learning
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Support vector machine based aerodynamic analysis of cable stayed bridges
Advances in Engineering Software
Multi-output regression on the output manifold
Pattern Recognition
Engineering Applications of Artificial Intelligence
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression
IEEE Transactions on Neural Networks
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In this paper, the complex relationship between environmental variables and dam static response is expressed using composition of functions, including nonlinear mapping and linear mapping. The environmental effect and noise disturbance is successfully separated from the monitoring data by analysis of the covariance matrix of multivariate monitoring data of dam response. Based on this separation process, two multivariate dam safety monitoring models are proposed. In model I, the upper control limits (UCLs) are calculated by performing kernel density estimation (KDE) on the square prediction error (SPE) of the offline data. For new monitoring data, we can judge whether they are abnormal by comparing the newly calculated SPE with the UCL. When abnormal data are detected, the SPE contribution plots and the SPE control chart of the new monitoring data are jointly used to qualitatively identify the reason for the abnormalities. Model II is a dam monitoring model based on latent variables that can be calculated from the separation process of the environmental and noise effects. The least squares support vector machines (LS-SVMs) model is adopted to simulate the nonlinear mapping from environmental variables to latent variables. The latent variables are predicted, and the prediction interval is calculated to provide a control range for the future monitoring data. The two monitoring models are applied to analyze the monitoring data of the horizontal displacement and hydraulic uplift pressure of a roller-compacted concrete (RCC) gravity dam. The analysis results demonstrate the good performance of the two models.