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<p>This study proposes a hybrid model based on variable mode decomposition (VMD) and temporal convolutional networks (TCNs). The VMD method was used to decompose the time series of landslide cumulative displacement data and environmental impact factors (reservoir level and rainfall) and the TCN model was used to make predictions. Experimental results show that this paper proposes that the VMD–TCN model has greater accuracy than other models, which improves the prediction accuracy and avoids the gradient disappearance problem in the long short-term memory models. It proves that the VMD–TCN model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.</p>

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Landslide Displacement Prediction Based on the VMD–TCN Hybrid Model

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Chunying Xu,
  • Kaibin Huang,
  • Chuliang Wei,
  • Xinjie Wu,
  • Ruixing Liang,
  • Yifei Dong

摘要

This study proposes a hybrid model based on variable mode decomposition (VMD) and temporal convolutional networks (TCNs). The VMD method was used to decompose the time series of landslide cumulative displacement data and environmental impact factors (reservoir level and rainfall) and the TCN model was used to make predictions. Experimental results show that this paper proposes that the VMD–TCN model has greater accuracy than other models, which improves the prediction accuracy and avoids the gradient disappearance problem in the long short-term memory models. It proves that the VMD–TCN model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.