# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 1 Introduction  
Structural safety is vital for buildings to function properly, and ensuring structural safety is crucial for the smooth operation of buildings. With the increasing number of power stations and reservoirs in operation, the safe operation of reservoir dams is receiving heightened attention. The safe operation of reservoir dams is not only important for engineering safety but also directly impacts the safety of people's lives and property [1, 2, 3]. Therefore, for reservoir dams, health diagnosis is a systematic and routine task. Along with global climate change, the frequency of extreme weather, earthquakes, and other disasters gradually increased, and water conservancy projects also face a series of new situations beyond the design state. Coupled with many factors, such as the deterioration of materials due to the growth of the building's operational life and the decline in the stability of the monitoring instrumentation, higher demands are placed on engineering safety [4, 5, 6]. Thus, it is extremely important to fully understand the operational status of the dam to ensure the safety of the dam project. Dam deformation serves as a reliable and intuitive measure of the overall effect, making it a crucial indicator for assessing and predicting the operational state of a dam. It ofteninvolves establishing a mathematical model that correlates dam deformation with various influencing factors, enabling the determination of the dam's health status [7]. Various regression methods based on mathematical statistical models are the most widely used research tools, but such methods cannot effectively solve the problem of multicollinearity among the influencing factors and are easily disturbed by uncertainties, resulting in poor model prediction accuracy, as the increasingly large and complex forms of prototype observations require efficient information mining to meet monitoring needs. With the continuous progress of dam safety monitoring theory and artificial intelligence technology, support vector machines (SVMs), relevance vector machines (RVMs), extreme learning machines (ELMs), and machine learning models based on various types of neural networks are applied in dam safety analysis and have shown unique advantages in solving the monitoring model factor uncertainty and nonlinearity problems. At the same time, they effectively improve the situation whereby the construction of water conservancy information emphasizes monitoring instruments and information integration systems. [PERSON]'s [8] SVM supervised learning model is considered to have significant advantages in nonlinear classification problems. [PERSON] et al. [9] used the SVM modeling method for the first time in safety state analysis of hydraulic buildings. Gaussian process as a machine language learning method has high adaptability in dealing with non-stationary data. [PERSON] et al. [10] established a regression model of the Gaussian process of temperature, which was effectively applied to water temperature prediction. In order to solve the problem that conventional machine learning cannot consider the effect of historical deformation behavior. [PERSON] et al. [4] used the long short-term memory (LSTM) algorithm proposed by [PERSON] [11] to establish a new dam deformation prediction model, which provides a high-performance, simultaneous, and dynamic prediction of the deformation behavior of concrete dams, extracting the time dimensional information from the target sequence. [PERSON] et al. [12] successfully addressed noise interference in monitoring sequences and enhanced prediction accuracy by integrating chaos theory, wavelet theory, and a radial basis function neural network. [PERSON] et al. [13] conducted a comparative study between the LSTM-MA and LSTM-PCA models and concluded that the LSTM-MA model is more convenient and suitable for engineering applications. [PERSON] et al.