A Deep Learning Approach Using Graph Convolutional Networks for Slope
Deformation Prediction Based on Time-series Displacement Data
Abstract
Slope deformation prediction is crucial for early warning of slope
failure, which can prevent property damage and save human life. Existing
predictive models focus on predicting the displacement of single
monitoring points based on time series data, without considering spatial
correlations among monitoring points, which makes it difficult to reveal
the displacement changes in the entire monitoring system, and ignores
the potential threats from nonselected points. To address the above
problem, this paper presents a novel deep learning method for predicting
the slope deformation, by considering the spatial correlations between
all points in the entire displacement monitoring system. The essential
idea behind the proposed method is to predict the slope deformation
based on the global information (i.e., the correlated displacements of
all points in the entire monitoring system), rather than based on the
local information (i.e., the displacements of a specified single point
in the monitoring system). In the proposed method, (1) a weighted
adjacency matrix is built to interpret the spatial correlations between
all points, (2) a feature matrix is assembled to store the time-series
displacements of all points, and (3) one of the state-of-the-art deep
learning models, i.e., T-GCN, is developed to process the above
graph-structured data consisting of two matrices. The effectiveness of
the proposed method is verified by performing predictions based on a
real dataset. The proposed method can be applied to predict
time-dependency information in other similar geohazard scenarios, based
on time-series data collected from multiple monitoring points.