Abstract: A prediction model of dam deformation monitoring data integrating Ensemble Empirical Mode Decomposition (EEMD), Genetic Algorithm (GA) and Back Propagation (BP) neural network is built to tackle the unstable performance and the drift of measured value of automatic monitoring data of dam deformation. The EEMD is used to extract the low-frequency signals which reflect the true deformation of dam and to remove the noise and outliers in the data of the automatic monitoring system; the GA-optimized BP neural network is employed to learn and extrapolate the real signals. The model-predicted deformation values are compared with measured values and also predicted values of some other methods in terms of residual error. Case study demonstrates that the proposed model could improve the prediction accuracy of dam deformation effectively.

Key words: dam deformation, prediction model, EEMD, BP neural network, genetic algorithm