浸润线的异常分布对土石坝的稳定性和安全性构成严重威胁。因此，准确预测浸润线成为土石坝安全监控的核心任务。然而，测压管监测数据存在非线性和非平稳性问题，这使得浸润线的预测模型容易出现过拟合，进而影响预测精度。为了解决这一问题，提出了一种基于SSA-LSTM模型的浸润线预测方法。该方法结合了麻雀搜索算法（sparrow search algorithm, SSA）和长短期神经网络（long-short-term neural Network, LSTM），通过优化模型的初始学习率和正则化参数，使输入数据与网络结构更好地匹配，从而提高预测精度。通过决定系数（R²）、平均绝对误差（mean absolute error, MAE）和均方根误差（root mean square error, RMSE）三个定量评价指标对模型预测结果进行了评估。结果表明，与传统的LSTM等模型相比，SSA-LSTM模型的预测精度有了显著提高，为土石坝的浸润线预测提供了有价值的参考。

The abnormal distribution of infiltration lines poses a serious threat to the stability and safety of earth and rock dams. Therefore, accurate prediction of the infiltration line has become a core task in the safety monitoring of earth and rock dams. However, there are nonlinear and non-smoothness problems in the monitoring data of the pressure tube, which makes the prediction model of the dip line prone to overfitting, which in turn affects the prediction accuracy. In order to solve this problem, a dip line prediction method based on SSA-LSTM model was proposed. The method combines SSA (sparrow search algorithm) and LSTM (long-short-term neural network) to improve the prediction accuracy by optimizing the initial learning rate and regularization parameters of the model to better match the input data with the network structure. The model prediction results were evaluated by three quantitative evaluation metrics, namely, coefficient of determination (R²), MAE (mean absolute error) and RMSE (root mean square error). The results show that the prediction accuracy of the SSA-LSTM model is significantly improved compared with the traditional LSTM and other models, which provides a valuable reference for the prediction of the dip line of earth and rock dams.