滑坡体变形预测对滑坡灾害治理具有重要的意义。根据边坡的演化特性，在研究得到滑坡是一个非线性动态系统，其监测数据具有分形特征的基础上，利用改进变维分形预测模型对滑坡体的监测数据时间序列趋势项进行预测，并引入人工神经网络对时间序列的偏离项进行纠偏优化，从而建立滑坡体监测数据的改进变维分形-人工神经网络（IVDFANN）耦合模型，并以茅坪滑坡体的实测位移为例进行预测。预测结果证明，该模型充分利用分形理论的自相似性和人工神经网络的自学习能力，具有良好的抗噪性，对小数据量的监测数据能够达到较高的预测精度和较好的预测长度，为滑坡体安全监控预测提供了新的参考方法。

The predication of landslide deformation is of great importance in landslide control and engineering construction. An Improved Variable Dimension Fractal-Artificial Neural Network (IVDF-ANN) coupling model is proposed for landslide monitoring. According to the characteristics of slope evolution, landslide is found to be a nonlinear dynamic system whose monitoring data are fractal. The model is established based on improved variable dimension fractal to predict the trend of time series in association with artificial neural network to optimize the deviation of time series. As a case study, the model is applied to the prediction of the displacement of Maoping Landslide with its in-situ displacement data. The result shows the model gives full display to the self-similarity of fractal theory and the self-learning ability of artificial neural network, and thus brings about vast range of prospect for application due to its high precision and noise immunity.