Abstract: A reasonable and credible dam deformation monitoring model is of great significance for scientific and effective analysis of dam deformation monitoring data and accurate and reliable evaluation of dam's working and operating conditions. The EEMD (Ensemble Empirical Mode Decomposition) model is adopted to decompose the dam deformation monitoring data, and the IMF (Intrinsic Mode Function) components representing different feature scales are obtained. With different influence factors for different components, the IMF components are used as the training samples of ELM (Extreme Learning Machine) to analyze, fit and predict the monitoring data. The predicted values of dam deformation are obtained by adding the values of each component. With a RCC (Roller Compacted Concrete) gravity dam as an example, the prediction result of EEMD-ELM model is compared with those of BPNN (Back Propagation Neural Network) model and ELM model. The comparison result reveal that the prediction accuracy of EEMD-ELM model is higher than that of BPNN model and ELM model, with the mean relative error merely 0.566, 54% and 14.8% lower than those of BPNN and ELM, respectively.

Key words: dam deformation, prediction model, EEMD, ELM, IMF