# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 1 Introduction  
Water energy is a clean, sustainable source of energy that is becoming increasingly significant. In order to make full use of water and energy, by the end of 2011, more than 98,000 reservoirs had been built in China. These dams have brought enormous social and economic benefits in many fields, such as power generation, water supply, shipping, and tourism. It has been crucial to the swift growth of the national economy [1]. Due to the influence of the natural environment and the lack of safety monitoring means and other factors, the working behavior of the dam will continue to change. It will bring some hidden dangers to the normal operation of the dam [2; 3]. If these hidden dangers cannot be found in time and measures taken to solve them, catastrophic accidents such as dam catastrophe or even dam failure are likely to be caused. Therefore, the research on ensuring the safe operation of dams has increasingly become the focus of scientific research workers. Dam deformation is one of the most important parameters to reflect dam safety. Scientific analysis and prediction of monitoring data are of great significance to ensure the safe operation of dams [1; 2; 3; 4]. However, the dam deformation data has strong nonlinearcharacteristics, and is affected by many factors, so a single-point prediction model used to predict the results is often not comprehensive enough. Therefore, it is necessary to combine the different characteristics of different models, and set up a combination of forecasting models to achieve more accurate prediction. [PERSON] et al. (2016) first proposed the theory of combined forecasting, which proposed that a single model could not solve the problem completely and needed to combine different models or methods in a scientific way, at the same time, a comprehensive forecasting model is obtained. Common combination prediction models include weighted average method, ensemble learning methods, neural network combination model, and time series combination model. Currently, the models based on weighted averaging generally use simple addition, which may reduce the accuracy of the prediction model. For neural network combination models, the commonly used method is parameter random initialization, which can lead to the model easily becoming stuck in local optima or not being sufficiently trained. For time series combination models, existing research often ignores the spatial correlation between multiple measuring points in the deformation sequence and fails to capture the dynamic correlation by combining the spatial and temporal dimensions. In view of the above problems, based on the idea of first decomposition, then integration, the major work of this paper is as follows:  
(1) For the single-point scenario, a combined prediction model based on IEALL is proposed to obtain the change trend of time dimension, and the time series data of single measurement points are analyzed to obtain the change trend of a dam. Additionally, a model parameter optimization algorithm IGWO (2017) is used to obtain the optimal parameters that minimized the model loss value and further improved the performance of the combined model. (2) For the scenario of multiple measurement points in the dam, a deformation prediction model STAGCN-IEALL is proposed. Graph Convolutional Networks (GCN) (2017; 2018) is used to extract the multipoint series space spatial features, which increases the expression ability of the model spatial dimension, and uses IEALL model to extract the change trend of the temporal dimension, thereby increasing the prediction accuracy of the model. STAGCN-IEALL also introduces a spatio-temporal attention mechanism (2018; 2019; 2020; 2021) to capture the dynamic correlation from both spatial and temporal dimensions. The remainder of this paper is as follows: Section 2 introduces the current research status and deficiencies in the deformation of dams. Section 3 introduces the methods and optimization ideas used in this paper. Section 4 introduces the dam deformation prediction model based on IEALL.