Landslide displacement prediction and interpretability analysis based on a graph spatiotemporal attention network

Accurate prediction of landslide displacement is central to early warning of geological disasters, yet existing data-driven models predominantly employ convolutional neural networks on regular grids, which fail to match the irregular spatial distribution of actual monitoring points and lack physical interpretability. In this paper, we propose a Graph Spatio-Temporal Attention Network (GSTA-Net) that uses a graph convolutional network to model the spatial structure of irregularly distributed moni