Displacement Prediction of the Muyubao Landslide Based on a GPS Time-Series Analysis and Temporal Convolutional Network Model

Remote Sensing

Xiaocheng Huang¹  Jun He²  Yixiang Song²  Yingquan Guo²  Zizheng Guo²  Da Huang² 

[1] School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, China;School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China;

关键词: landslide displacement prediction;    GPS time-series analysis;    temporal convolutional network;    long short-term memory neural network;    salp swarm algorithm;   

DOI  :  10.3390/rs14112656

来源: DOAJ

【 摘 要 】

Landslide displacement prediction is an essential base of landslide hazard prevention, which often needs to establish an accurate prediction model. To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed. The TCN model, consisting of a causal dilation convolution layer residual block, can flexibly increase the receptive fields and capture the global information in a deeper layer. SSA can solve the hyperparameter problem well for TCN model. The Muyubao landslide displacement collected from a professional GPS monitoring system implemented in 2006 is used to analyze the displacement features of the slope and evaluate the performance of the SSA-TCN model. The cumulative displacement time series is decomposed into trend displacement (linear part) and periodic displacement (nonlinear part) by the variational modal decomposition (VMD) method. Then, a polynomial function is used to predict the trend displacement, and the SSA-TCN model is used to predict the periodic displacement of the landslide based on considering the response relationship between periodic displacement, rainfall, and reservoir water. This research also compares the proposed approach results with the other popular machine learning and deep learning models. The results demonstrate that the proposed hybrid model is superior to and more effective and accurate than the others at predicting the landslide displacement.

【 授权许可】

Unknown