Landslide deformation characterized with step-like curves often presents periodicity implicitly. This paper proposed a novel data-driven approach that adopted periodic neural network (PNN) and variational mode decomposition (VMD) to conduct displacement prediction based on the intrinsic seasonality of step-like landslide displacement. PNN was a novel neural network designed for capturing the seasonality of the time series. Firstly, the initial displacement would be decomposed into trend component, periodic component, and random component using the variational mode decomposition (VMD). Then, the external triggering factors were also decomposed by VMD into several subsequences. Subsequences with periodic and random characteristics were selected as the input datasets to forecast the periodic and random components by PNN. Finally, the total displacement was obtained by superimposing the three predictive components to validate the model performance. The Baishuihe landslide was taken as a case study to validate the high effectiveness and efficiency of our method. The result proved that our new model presented satisfactory prediction accuracy without complex training process. Meanwhile, PNN performed a strong robustness to the missing values due to the advantage of its structure. In addition, we clarified a corrective data processing mode as “strict” mode: the dataset has to be divided into training and validation sets firstly to avoid the leakage of the future data.

具有阶梯状曲线特征的滑坡变形通常隐含呈现周期性。本文提出了一种新的数据驱动方法，该方法采用周期性神经网络（PNN）和变分模式分解（VMD）进行基于阶梯状滑坡位移固有季节的位移预测。PNN是一种新颖的神经网络，旨在捕获时间序列的季节性。首先，将使用变分模式分解（VMD）将初始位移分解为趋势分量，周期​​分量和随机分量。然后，外部触发因素也被VMD分解为几个子序列。选择具有周期和随机特征的子序列作为输入数据集，以通过PNN预测周期和随机分量。最后，总位移是通过叠加三个预测分量来验证模型性能而获得的。以白水河滑坡为例，验证了该方法的有效性和有效性。结果证明，我们的新模型无需复杂的训练过程即可提供令人满意的预测精度。同时，由于其结构的优势，PNN对缺失值具有很强的鲁棒性。此外，我们将纠正性数据处理模式澄清为“严格”模式：必须首先将数据集分为训练集和验证集，以避免将来的数据泄漏。结果证明，我们的新模型无需复杂的训练过程即可提供令人满意的预测精度。同时，由于其结构的优势，PNN对缺失值具有很强的鲁棒性。此外，我们将纠正性数据处理模式澄清为“严格”模式：必须首先将数据集分为训练集和验证集，以避免将来的数据泄漏。结果证明，我们的新模型无需复杂的训练过程即可提供令人满意的预测精度。同时，由于其结构的优势，PNN对缺失值具有很强的鲁棒性。此外，我们将纠正性数据处理模式澄清为“严格”模式：必须首先将数据集分为训练集和验证集，以避免将来的数据泄漏。