An adaptive spatial–temporal prediction model for landslide displacement based on decomposition architecture

Abstract

Landslide displacement forecasting is a core issue in geohazard research, it is particularly challenging for accumulation-type landslides with complex geological patterns. Traditional landslide displacement prediction methods use single-point modeling and often fail to consider the spatial correlation characteristics of each deformation point on the surface of a landslide. On the other hand, they have difficulty in learning the changes caused by rainfall and reservoir water level. To tackle these obstacles, we introduce an adaptive spatial–temporal landslide displacement prediction model based on a decomposition architecture, named Self-Adaptive Unet with Decomposed Temporal Attention Encoder(SAU-DTAE). To effectively separate the features of different scales in time series changes and model them separately, we employ a progressive decomposition architecture based on a Lightweight Temporal Attention Encoder(LTAE). Furthermore, we design a gating mechanism with Sample Entropy (SampEn) to adaptively extract global and local spatial features at multiple scales. By quantifying the spatial complexity, we can achieve adaptive extraction of spatial correlation features. Relevant experiments were conducted with the 2016-2023 Interferometry Synthetic Aperture Radar (InSAR) landslide displacement dataset of the Three Gorges area. The new proposed algorithm was compared and validated against several classical time-series prediction models: Back Propagation(BP) neural network, Long Short Term Memory(LSTM) neural network, Gated Recurrent Unit(GRU), Convolutional LSTM(ConvLSTM), Informer, and Autoformer. The findings from the experiment indicated that our model surpassed the benchmark models, achieving superior prediction results on the test set. The Mean Absolute Error (MAE) was 5.516 millimeters(mm), the Root Mean Square Error (RMSE) was 3.856 mm, and the R-Square($R^2$) was 0.896.