Causality-Preserving Multiscale and Meteorological-Aware Transformer for Three-Dimensional Open-Pit Slope Displacement Forecasting

Short-horizon prediction of three-dimensional slope displacement is important for proactive risk control in open-pit mines, where deformation commonly exhibits multiscale, non-stationary, and environmentally responsive characteristics. This study aims to develop a causality-preserving framework for direct 24 h prediction of northward, eastward, and upward slope displacement while jointly representing multiscale deformation patterns, meteorological influences, and temporal dependencies. A Variational Mode Decomposition-Gated Recurrent Unit-Cross-Attention-Transformer Network (VGCAT-Net) is therefore proposed. Window-wise variational mode decomposition constructs multiscale displacement features from each 168 h historical window, gated recurrent units encode local temporal evolution, meteorological cross-attention dynamically integrates environmental information, and a Transformer captures long-range temporal dependencies. The framework was evaluated using GNSS displacement observations from four monitoring stations and synchronized meteorological measurements at the Fushun West Open-Pit Mine. At the reference station FS-01, VGCAT-Net achieved an average mean absolute error of 4.538 mm, root mean square error of 5.642 mm, and coefficient of determination of 0.853, outperforming the RF, LSTM, GRU, and Transformer baselines. Cross-site evaluation yielded mean R2 values of 0.739–0.831. These results demonstrate that combining multiscale deformation representation with state-conditioned meteorological fusion can improve stable one-day-ahead three-dimensional displacement forecasting and provide useful predictive information for operational slope monitoring and early-warning support.