## 1 Introduction  
Dams can bring significant socio-economic benefits under safe operating conditions. In case of a dam accident, there will be a huge disaster [1; 2; 3]. In fact, most dam accidents did not arise suddenly, but went through a process from quantity to quality variation [4]. If we can establish suitable prediction models for dam monitoring data and analyze them in a timely manner, potential problems in the structural behavior of dams will be identified, thus avoiding accidents. As the controlled indicator of dam safety monitoring, deformation monitoring data can objectively reflect the structural state and the safety condition of dams, which are one of the important bases for assessing the safety of dam projects [5]. During the actual service of a dam, the deformation monitoring data are usually complex nonstationary and nonlinear time series. Therefore, it is an important research topic to accurately predict dam deformation (DD) in the future by using historical deformation monitoring data [6; 7]. Currently, the commonly used models for predicting DD are the deterministic models, statistical models, neural network models, and hybrid models [7; 8; 9; 10; 11; 12; 13]. The deterministic models explain DD based on the physical laws of loads, material properties, and stress-strain relationships [6; 14], requiring as accurate as possible material parameters, geometry, and operating conditions of the dam and its foundation, which are difficult to achieve in actual engineering [15; 16]. In contrast, a statistical model is more easily implemented [13]. It is a more commonly used data-driven approach for DD monitoring [17]. Although the statistical model is simple and effective, it cannot capture the nonlinear characteristics of the DD time series, thus affecting the accuracy of deformation prediction [14]. In comparison, a neural network model has unique advantages in dealing with nonlinear problems [18; 19]. In the field of DD prediction, the neural network models have good and robust prediction performance. This so-called 'black box' method can build a data-driven model based on the historical deformation data of dams, thereby achieving high-precision prediction of deformation without knowing detailed environmental information, avoiding complex data handling and modeling processes. Among them, the artificial neural networks (ANN) [20] and support vector machines (SVM) [21] are the most commonly used methods for the nonlinear problems. However, these models have some limitations [5]. An ANN model based on gradient descent requires multiple iterations to correct the relevant parameters, which is a time-consuming process and the model tends to fall into local minima. Although an SVM overcomes the shortcomings of the ANN model, its kernel function parameters are difficult to choose [1]. Therefore, in this paper, the extreme learning machine (ELM) [22; 23; 24] with efficient regression capabilities is chosen as the basic model for DD prediction to address the drawbacks of the above models [25; 26]. It randomly generates connection weights between the input and hidden layers and thresholds for neurons in the hidden layer without adjusting during the training process. The conditions for the unique optimal solution of the ELM output are the determined activation function and the hidden layer parameters. Compared with traditional methods, ELMs have the advantages of fast learning speed and good generalization performance [27]. Although the ELM model has some advantages for handling nonlinear problems, the strong volatility of the deformation data will undoubtedly have negative impacts on the prediction results. Therefore, before predicting the DD, we need to preprocess the time series by signal decomposition technique, which can extract useful information from the original data to improve the prediction accuracy of data-driven models.