联合聚类方法和深度学习的混凝土坝变形预测

混凝土大坝变形预测对其安全运行具有重要意义，针对传统分析方法难以捕捉长期序列时序特征从而导致预测精度较低的问题，本文采用麻雀优化算法（SSA）和K调和均值算法（KHM）相结合对监测值进行聚类以捕捉序列时序特征，然后采用自适应噪声完备集合经验模态分解（CEEMDAN）等方法对聚类结果进行降噪处理，最后采用长短期记忆（LSTM）模型对序列进行预测。分析结果表明，本文所提出的聚类方法具有较好的长序列特征识别能力，结合基于CEEMDAN分解方法去除序列中存在的冗余信息，从而使LSTM模型能够更好地捕捉变形值的时序特性，进而提高预测精度。所提模型具有较好的精度和适应性，可为大坝变形预测提供一种有效方法。

Predictions of concrete dam deformation using clustering method and deep learning

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.