A spatio-temporal network for landslide displacement prediction based on deep learning

doi:10.11947/j.AGCS.2022.20220297

Abstract

Abstract: Landslide deformation monitoring data is the direct basis for understanding the evolution law of landslide deformation, and the deep mining of this data is a powerful guarantee to realize the early warning and prediction of landslide disaster. The existing landslide prediction models are mostly limited to the time-series displacement prediction of a single monitoring point and do not consider the spatial correlation among monitoring points. To address the above problems, this paper proposes a spatio-temporal prediction model for landslide displacement based on deep learning: Firstly, the weighted adjacency matrix expressing the spatial correlation among all points in the interpretation is constructed; Secondly, the external influences are introduced to strengthen the attribute feature matrix in order to construct the graph structure data; Finally, this model of ensemble graph convolutional network (GCN) and gate recurrent unit (GRU) is used, and the optimal hyper-parameters are found through multiple sets of experiments .Compared with the comparison model, the root mean square error(RMSE) of the proposed model is 4.429 mm, which is at least 47.3% lower. The ablation experiment results also show that the attribute augmentation with the introduction of external influences can further improve the prediction performance of the model, and the RMSE is reduced by 28.4% compared with the results without attribute augmentation. The results suggest that the method can be used for spatio-temporal prediction of landslide displacements or other observed quantities in geological hazards that also have spatio-temporal correlation properties.

Key words: landslide, graph convolution network, time series prediction, gate recurrent unit, spatial correlation

CLC Number:

P227

LUO Huiyuan, JIANG Yanan, XU Qiang, LIAO Lu, YAN Aoxiang, LIU Chenwei. A spatio-temporal network for landslide displacement prediction based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2160-2170.

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