Gaussian process regression-based forecasting model of dam deformation

Abstract

The displacement at various measurement points is a critical indicator that can intuitively reflect the operational properties of a dam. It is important to analyse displacement monitoring data in a timely manner and make reliable predictions of dam safety. This paper proposes a GPR-based model for dam displacement forecasting. The input variables of the monitoring model consider hydraulic factors, thermal factors and irreversible factors, and the output variables are the observed displacements of the dam. An example analysis based on the proposed method is performed on a prototype gravity dam, and the performance of different simple/combined covariance functions is investigated to obtain the optimal choice. Compared to multiple linear regression, radial basis function network (RBFN) and support vector machine (SVM) methods, the results indicate that the GPR-based model with a combined covariance function significantly improves the prediction accuracy. The proposed model can effectively overcome the over-learning and poor robustness issues of approaches such as RBFN and SVM. In addition, the GPR-based forecasting model has the advantages of simplicity in the training process and the capacity to provide a probabilistic output.