Paper Info
Title
A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations
Abstract
Landslide displacement prediction is crucial for the early warning of slope failure but remains a challenging task due to its spatiotemporal complexity. Although temporal dependency has been well studied and discussed, spatial dependence is relatively less explored due to its significant variations of the spatial structure of landslides. In this study, a novel graph convolutional incorporating GRU network (GC-GRU-N) is proposed and applied to landslide displacement forecasts. The model conducts attribute-augmented graph convolution (GC) operations on GNSS displacement data with weighted adjacency matrices and an attribute-augmented unit to combine features, including the displacements, the distance, and other external influence factors to capture spatial dependence. The output of multi-weight graph convolution is then applied to the gated recurrent unit (GRU) network to learn temporal dependencies. The related optimal hyper-parameters are determined by comparison experiments. When applied to two typical landslide sites in the Three Gorge Reservoir (TGR), China, GC-GRU-N outperformed the comparative models in both cases. The ablation experiment results also show that the attribute augmentation, which considers external factors of landslide displacement, can further improve the model's prediction performance. We conclude that the GC-GRU-N model can provide robust landslide displacement forecasting with high efficiency.
Year
DOI
Venue
2022
10.3390/rs14041016
REMOTE SENSING
Keywords
DocType
Volume
spatiotemporal analysis, landslide displacement prediction, attribute-augmented, deep learning
Journal
14
Issue
Citations 
PageRank 
4
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Yanan Jiang100.34
Huiyuan Luo200.34
Qiang Xu311.39
zhong lu44018.20
Lu Liao500.34
Huajin Li621.39
Lina Hao700.34