基于BP-PCA-WCA-SVM的混凝土大坝变形预测方法

A Method of Predicting Concrete Dam Deformation Based on BP-PCA-WCA-SVM

传统基于单一模型的混凝土大坝变形预测方法预测精度低,噪声稳健性差,泛化能力弱。为解决该问题,提出一种基于贝塔先验主成分分析(BP-PCA)与水循环算法(WCA)优化支撑向量机(SVM)相结合的混凝土大坝变形组合预测方法。首先利用所提BP-PCA模型对变形数据进行多尺度降噪分解,将复杂非线性、非平稳随机过程分解为一系列结构简单的主分量;然后利用WCA优化的SVM(WCA-SVM)对每个主分量分别建立预测模型;最后将多个主分量的预测结果综合叠加得到最终预测结果。以我国中部地区某混凝土大坝变形监测数据开展试验,结果表明,所提BP-PCA模型能够有效挖掘数据中隐含的趋势性和规律性信息,BP-PCA-WCA-SVM模型能够获得较高的预测精度,预测结果的相对误差为1.07%,误差均方根为0.065。相对于Kalman滤液、SVM、CNN 3种方法,所提模型预测性能提升均超过62%,并且具有更强的噪声稳健性和泛化能力。

Traditional single-model prediction methods suffer from issues like low accuracy,susceptibility to noise,and limited generalization capability.To address these challenges,we propose a novel approach for predicting concrete dam deformation by integrating the Beta Prior Principal Component Analysis(BP-PCA)and the Water Cycle Algorithm(WCA).Initially,the BP-PCA model decomposes deformation data into multiple scales,effectively reducing noise.This decomposition transforms the intricate nonlinear and non-stationary stochastic process into a set of principal components with simplified structures.Simultaneously,it enhances noise robustness by suppressing noise during the decomposition process.Subsequently,we employ the Water Cycle Algorithm optimized Support Vector Machine(WCA-SVM)to construct prediction models for each principal component.Finally,we integrate the prediction outcomes from multiple principal components to derive the final prediction result.The relative prediction error is minimized to 1.07%,with a root mean square error of 0.065.Compared to the three methods included in the comparative analysis,our approach yields over 62%improvement in prediction performance,demonstrating superior noise robustness and generalization capability.