Displacement prediction is a direct and effective method for mitigating geohazards. Due to the influence of rainfall and reservoir water level variations, landslides often display step-like deformations with an increasing trend and periodic fluctuation, indicating long-term memory in displacement time series. Traditional data-driven methods are mostly suitable for short-term prediction, and extra data processing is applied to solve this problem. This paper proposes a novel deep learning-based displacement prediction method using long short-term memory (LSTM) networks. Based on open-source frameworks for deep learning, namely, Keras and TensorFlow, a detailed implementation of displacement prediction is proposed and illustrated. The Baishuihe landslide, a typical landslide with long-term monitoring, is taken as a case study, and both single-factor and multi-factor predictions are performed. The results indicate that multi-factor prediction can reduce overfitting and improve accuracy. Compared with the existing method, the multi-factor deep-learning model displays better performance. This study indicates that the LSTM-based deep-learning model is suitable and convenient for displacement prediction and has broad prospects in safety monitoring of water-induced landslides.

位移预测是减轻地质灾害的直接有效方法。由于降雨和水库水位变化的影响，滑坡经常表现出阶梯状的变形，并具有增加的趋势和周期性的波动，表明在位移时间序列中具有长期记忆。传统的数据驱动方法最适合短期预测，并应用额外的数据处理来解决此问题。本文提出了一种使用长短期记忆（LSTM）网络的新型基于深度学习的位移预测方法。基于深度学习的开源框架Keras和TensorFlow，提出并说明了位移预测的详细实现。以长期监测的典型滑坡白水河滑坡为例，同时执行单因素和多因素预测。结果表明，多因素预测可以减少过拟合并提高准确性。与现有方法相比，多因素深度学习模型具有更好的性能。这项研究表明，基于LSTM的深度学习模型适用于位移预测，并且方便，并且在水诱发滑坡的安全监测中具有广阔的前景。