Multi-Scale Landslide Displacement Prediction and Multi-Level Early Warning for the Three Gorges Reservoir Area Using Multi-Source Sensing and Gated-Attention Multimodal Fusion.

Landslides are severe global geological hazards, and landslide displacement prediction is essential for early warning. Focusing on landslides induced by the coupling of periodic reservoir water level fluctuations and local short-term rainstorms in the Three Gorges Reservoir Area (TGRA), existing early-warning methods suffer from coarse rainfall spatial representation, static multimodal-feature fusion, and insufficient deformation-stage-based hierarchical warning mechanisms. Based on multi-source sensing data including Global Positioning System (GPS) displacement monitoring, reservoir water level sensors, ground rain gauges, meteorological radar, and multi-timescale rainfall forecast maps, this study integrates multimodal time-series records of multiple landslide sites from 2007 to 2024. Particle Swarm Optimization-Kriging (PSO-Kriging) interpolation and rainfall-map gridding were employed to construct a dual-source fine-grained rainfall reconstruction scheme. The Bidirectional Long Short-Term Memory (BiLSTM)-Attention network extracted deep temporal features, and a gated-attention fusion mechanism realized 1 d-15 d multi-scale landslide displacement prediction. Combined with the five-stage landslide creep theory, a four-level early-warning system was established. Experimental results show that the proposed multimodal-fusion model achieves a Root Mean Square Error (RMSE) of 3.42 ± 1.27 mm for the 1 d prediction horizon. The model F1-score reaches 89.3 ± 1.9% for short-term warning and 81.7 ± 1.2% for medium-and-long-term warning, which fits well with long-term reservoir-monitoring scenarios. This framework provides feasible technical support for reservoir landslide hazard prevention using multi-source sensing.