Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery

Abstract

A key component of disaster management and infrastructure organization is predicting cumulative deformations caused by landslides. One of the critical points in predicting deformation is to consider the spatio-temporal relationships and interdependencies between the features, such as geological, geomorphological, and geospatial factors (predisposing factors). Using algorithms that create temporal and spatial connections is suggested in this study to address this important point. This study proposes a modified graph convolutional network (GCN) that incorporates a long and short-term memory (LSTM) network (GCN-LSTM) and applies it to the Moio della Civitella landslides (southern Italy) for predicting cumulative deformation. In our proposed deep learning algorithms (DLAs), two types of data are considered, the first is geological, geomorphological, and geospatial information, and the second is cumulative deformations obtained by permanent scatterer interferometry (PSI), with the first investigated as features and the second as labels and goals. This approach is divided into two processing strategies where: (a) Firstly, extracting the spatial interdependency between paired data points using the GCN regression model applied to velocity obtained by PSI and data depicting controlling predisposing factors; (b) secondly, the application of the GCN-LSTM model to predict cumulative landslide deformation (labels of DLAs) based on the correlation distance obtained through the first strategy and determination of spatio-temporal dependency. A comparative assessment of model performance illustrates that GCN-LSTM is superior and outperforms four different DLAs, including recurrent neural networks (RNNs), gated recurrent units (GRU), LSTM, and GCN-GRU. The absolute error between the real and predicted deformation is applied for validation, and in 92% of the data points, this error is lower than 4 mm.

Instrumental Monitoring of a Slow-Moving Landslide in Piedmont (Northwest Italy) for the Definition of Rainfall Thresholds

“…Landslides represent one of the most serious hazards in many areas of the world. Their activation can be related to several factors, including rainfall, earthquakes, snowstorms and human activities (e.g., [ 1 , 2 , 3 , 4 , 5 , 6 ]). In large portions of the Italian territory, landslide susceptibility is high to very high due to the combination of morphological, geological and climatic factors, and to the additional effect of human activity (e.g., [ 7 , 8 , 9 , 10 ]).…”

Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models

“…Moreover, currently, GCN, as an excellent deep learning algorithm, has rarely been adopted in the landslide area. Recently, owing to precision and universality advantages, a few studies applied it to predict landslide displacements (Jiang et al, 2022;Khalili et al, 2023;Ma et al, 2021), to evaluate landslide susceptibility (Du et al, 2021;Wang, Du, et al, 2023;Xia et al, 2024), or to detect landslides (Li et al, 2023). In this work, GCN is for the first time employed to forecast deformation stages of a landslide.…”

Updated Global Navigation Satellite System Observations and Attention-Based Convolutional Neural Network–Long Short-Term Memory Network Deep Learning Algorithms to Predict Landslide Spatiotemporal Displacement

“…Some spatiotemporal displacement prediction approaches have been previously introduced. For instance, Khalili et al [27] introduced an adapted graph convolutional network (GCN), which integrated LSTM architecture. This hybrid approach was deployed in the context of forecasting the cumulative deformation for landslides in Italy.…”