Research on Dam Deformation Prediction Model Based on CEEMDAN-GMDH-ARIMA
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.
Complete Ensemble Empirical Mode Decomposition with Adaptive NoiseGroup Method of Data HandlingAutoregressive Inte-grated Moving Average Modeldamdeformation predictionJiangxi Shangyoujiang Hydropower Station
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