Abstract: The deformation prediction of a concrete dam is important to its safe operation. To solve the problem of low prediction accuracy of traditional analysis methods resulted from the difficulty in capturing the characteristics of long-term sequences, this paper uses a combination of Sparrow Search Algorithm (SSA) and the K-Harmonic Mean (KHM) algorithm to cluster the monitored values and capture the long-sequence features. Then, we use methods such as Complete Ensemble Empirical Mode Decomposition (CEEMDAN) to reduce the noise in the clustered data, and a long short-term memory (LSTM) model to predict long sequences. The analysis results show this clustering method has a better capability of identifying long-sequence features. It removes the redundant information from the sequence by cooperating with the CEEMDAN decomposition-based method, and enables the LSTM model to better capture the time-sequence characteristics of dam deformation, thus improving the prediction accuracy significantly. The proposed method is good in accuracy and adaptability and useful for dam deformation prediction.

Key words: concrete dam deformation, K-harmonic mean algorithm, sparrow search algorithm, complete ensemble empirical mode decomposition with adaptive noise, long short-term memory