大坝变形预测的准确性对大坝结构稳定和整体安全至关重要。近年来，为提升预测精度，优化算法广泛应用于大坝预测模型。然而，传统优化算法易陷入局部最优解，制约模型性能。为此，引入一种随机搜索机制至灰狼算法（grey wolf optimizer, GWO），通过Metropolis接受准则进一步改进GWO，优化算法性能。然后，创新性地将经验模态分解（empirical mode decomposition, EMD）、改进灰狼算法（modify GWO, MGWO）以及长短期记忆网络（long short-term memory network, LSTM）相融合构建一个先进的大坝沉降预测模型。以新疆五一水库实测数据作验证，采用EMD对实测数据进行处理，深入分析各分量不同的变化特征；随后，利用MGWO对LSTM的超参数精确调优，实现大坝沉降的精准预测。最后将EMD分解前后模型进行了对比分析。结果表明，提出的EMD-MGWO-LSTM大坝沉降预测模型在4个误差性能指标上均表现出显著优势，具有更高的拟合精度和卓越的预测性能。研究成果增强了其适应性，在复杂多变的大坝动态运行环境中仍然能够保持快速的响应和准确的预测，为大坝安全监测与预警提供技术支撑，有力地推动了水利现代化防洪减灾技术的发展与升级。

The accuracy of dam deformation prediction is crucial for the structural stability and overall safety of dams. In recent years, optimization algorithms have been widely used to enhance the prediction accuracy of dam models. However, traditional optimization algorithms often get trapped in local optima, which limits the performance of the models. To address this issue, a stochastic search mechanism was introduced into the grey wolf optimizer (GWO), and the GWO was further improved through the Metropolis acceptance criterion to optimize its performance. Subsequently, an advanced dam settlement prediction model was innovatively constructed by integrating empirical mode decomposition (EMD), modified grey wolf optimizer (MGWO), and long short-term memory network (LSTM). The model was validated using actual data from the Wuyi reservoir in Xinjiang, where EMD was applied to process the data, and a detailed analysis of the different variation characteristics of each component was conducted. Then, the hyperparameters of the LSTM were finely tuned using the MGWO to achieve accurate prediction of dam settlement. A comparative analysis of the model before and after EMD decomposition was conducted. The results showed that the proposed EMD-MGWO-LSTM model for dam settlement prediction exhibited significant advantages across four error performance indicators, demonstrating higher fitting accuracy and superior predictive performance. The findings enhance its adaptability, maintaining fast response and accurate predictions in the complex and variable dynamic operating environment of dams. This research result provides technical support for dam safety monitoring and early warning, vigorously promoted the development of water conservancy modernization of flood control and disaster mitigation technology and upgrade.