Accurate displacement prediction is critical for the early warning of landslides. The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult. Moreover, in engineering practice, insufficient monitoring data limit the performance of prediction models. To alleviate this problem, a displacement prediction method based on multisource domain transfer learning, which helps accurately predict data in the target domain through the knowledge of one or more source domains, is proposed. First, an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend, periodic, and stochastic components. The trend component is predicted by an autoregressive model, and the periodic component is predicted by the long short-term memory. For the stochastic component, because it is affected by uncertainties, it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy. Considering a real mine slope as a case study, the proposed prediction method was validated. Therefore, this study provides new insights that can be applied to scenarios lacking sample data.

准确的位移预测对于滑坡预警至关重要。多种影响因素与位移耦合关系的复杂性使得位移的准确预测变得困难。此外，在工程实践中，监测数据不足限制了预测模型的性能。为了缓解这一问题，提出了一种基于多源域迁移学习的位移预测方法，该方法有助于通过一个或多个源域的知识准确预测目标域中的数据。首先，使用基于最小样本熵的优化变分模态分解模型将累积位移分解为趋势、周期和随机分量。趋势分量由自回归模型预测，周期成分由长短期记忆预测。对于随机成分，由于它受到不确定性的影响，因此通过 Wasserstein 生成对抗网络和多源域迁移学习相结合进行预测，以提高预测精度。以实际矿山边坡为例，对所提出的预测方法进行了验证。因此，这项研究提供了新的见解，可以应用于缺乏样本数据的场景。