A Hybrid Model Integrating Principal Component Analysis, Fuzzy C-Means, and Gaussian Process Regression for Dam Deformation Prediction

摘要

Dam behavior predictionmodel is a fundamental component of dam structural health monitoring systems. As themost intuitivemonitoring indicators, deformation is commonly used to reflect the dam behavior change. The selection of input variablesand training samples determines the performance of dam deformation predictive models. In this paper, a novel hybrid modelintegrating principal component analysis (PCA), fuzzy C-means (FCM), and Gaussian process regression (GPR) are proposedto predict dam deformation. Specifically, PCA is utilized to extract the main information of original thermometer data astemperature variables, while FCM is used to divide the samples into several categories according to the similarity of theenvironmental monitoring data. Then, the samples in each category are used to train GPR models with five commonly usedcovariance functions based on influencing factors, respectively. In the test phase, FCM is used to determine what categorythe samples in the test set belong to, and then, the corresponding trained GPR model is utilized to predict dam deformation.The proposed hybrid model is fully demonstrated and validated by monitoring data collected from a multiple-arch concretedam in long-term service. Various benchmark models with or without FCM analysis are selected as comparison models.Experimental results show the proposed novel model outperforms the other comparison methods in terms of all evaluationindicators. This indicates fuzzy clustering analysis can effectively improve the performance of the prediction model, and theproposed hybrid model can predict future dam deformation with high accuracy and efficiency.