人民黄河2024，Vol.46Issue(1)：146-150,5.DOI:10.3969/j.issn.1000-1379.2024.01.025
基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型研究
Research on Dam Deformation Prediction Model Based on CEEMDAN-GMDH-ARIMA
摘要
Abstract
In order to improve the accuracy of dam deformation prediction,in view of the complexity and nonlinear characteristics of dam de-formation data,a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Group Method of Data Handling(GMDH)and Autoregressive Integrated Moving Average Model(ARIMA)were used to conduct research on dam deformation prediction.CEEMDAN was used to decompose the original dam data deformation into high-frequency random components,medium-frequency periodic components and low-frequency trend components,and then the GMDH model and ARIMA model were used to predict the high-medium fre-quency components and low-frequency components respectively.Dam Deformation Prediction Model Based on CEEMDAN-GMDH-ARIMA was established.Taking Jiangxi Shangyoujiang Hydropower Station as an example,the prediction results of this model were compared with the prediction results of back propagation(BP),radial basis function(RBF),GMDH and CEEMDAN-GMDH models.The results show that the root mean square error(ERMS),mean absolute error(EMA),and correlation coefficient(r)of the CEEMDAN-GMDH-ARIMA model are 0.048 mm,0.035 mm,and 0.994,respectively,which are superior to the BP,RBF,GMDH,and CEEMDAN-GMDH models.The model has the best prediction performance and can well reflect the trend of horizontal displacement changes at monitoring points.关键词
自适应噪声完备集成经验模态分解/数据处理群集法/差分自回归移动平均模型算法/大坝/变形预测/江西上犹江水电站Key words
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise/Group Method of Data Handling/Autoregressive Inte-grated Moving Average Model/dam/deformation prediction/Jiangxi Shangyoujiang Hydropower Station分类
建筑与水利引用本文复制引用
程小龙,张斌,刘相杰,刘陶胜..基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型研究[J].人民黄河,2024,46(1):146-150,5.基金项目
国家自然科学基金青年科学基金项目(42004158) （42004158）
