# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 1 Introduction
[14] compared multiple models and found that their proposed RSFAS-bi-directional LSTM model had a shorter execution time, which proved beneficial in determining parameters for different seasonal decision processes. [PERSON] et al. [15] constructed a new model based on iterative amplitude adjusted Fourier transform (IAAFT) and bi-directional long short-term memory (BiLSTM) for classifying mild cognitive impairment (MCI) and a healthy control (HC) group. They discovered that BiLSTM was better suited for EEG classification tasks, and the proposed model achieved higher accuracy standards. [PERSON] et al. [16] fused a U-net neural network algorithm and improved image refinement algorithm to propose a method for dam crack recognition and width calculation, which improves the recognition accuracy of fine cracks without relying on a large number of training samples. Although the existing models have greatly improved the prediction accuracy of deformation models, there are still some limitations, such as the complex structure of long short-term memory (LSTM) models, slow computation speed, and inability to iterate the global optimal information. When establishing a predictive model based on neural networks, the model is influenced by various factors such as data frequency and complexity. It can only obtain a relatively suitable parameter combination through extensive trials and constant debugging. However, as the dataset grows, data complexity increases, neural network layers become more numerous, and such an approach becomes increasingly inefficient. The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm that simulates sparrow foraging behavior and anti-predation behavior. It outperforms existing algorithms in terms of search accuracy, convergence speed, stability, and avoidance of local optima. In particular, it exhibits excellent global optimization capabilities in complex solving environments. Relevant studies indicate that the SSA algorithm surpasses other optimization algorithms such as the gray wolf optimizer (GWO), particle swarm optimization (PSO), and the gravitational search algorithm (GSA) [17; 18; 19]. LSTM models are more capable of handling longer time series data and coping with gradient disappearance effectively and are therefore among the preferred analytical models in dam monitoring data analysis [20; 21]. Combining SSA algorithms with LSTM models will give better results in model prediction, multi-objective optimization and parameter optimization. In the context of time series dam monitoring data, quickly identifying optimal data through SSA can effectively address convergence and prediction issues in monitoring models, reducing occurrences of non-convergence and poor fitting. To fully explore and predict the temporal correlation of dam monitoring data, this study combines the global optimization advantage of the sparrow search algorithm (SSA) and the predictive power of the long short-term memory (LSTM) neural network model. A hybrid model for dam deformation prediction based on SSA and LSTM is proposed, which not only determines the optimal position of hyperparameters to optimize the LSTM model but also ensures the accuracy of the model's predictions.