A Multi-Graph Attention Fusion Network for Dam Deformation Prediction Under Data Missing Conditions

Dam deformation monitoring is essential for ensuring the safe operation of hydraulic structures, yet practical data are often compromised by missing values and noise, while spatial coupling among monitoring points further complicates prediction. To address these challenges, this study proposes a Spatio-Temporal Multi-Graph Attention Fusion Network (STMGAFN) for dam deformation prediction and risk early warning under incomplete data conditions. Data quality is enhanced through a data-quality-aware hierarchical adaptive imputation mechanism combined with a VMD–wavelet joint denoising strategy. A multi-graph spatial modeling framework integrating temporal similarity, spatial proximity, structural zoning, and measuring-line connectivity is constructed, and fuses multi-source spatial features through a lightweight adaptive attention mechanism. A parameter-sharing recursive probabilistic temporal modeling approach is adopted to jointly predict deformation values and their associated uncertainties. Based on the predicted confidence intervals, a four-level risk classification and early-warning scheme is established. Experimental results on real GNSS monitoring data from dam sites demonstrate that the proposed method achieves an RMSE of 0.3588 mm, an MAE of 0.1738 mm, and an R2 of 0.9865, outperforming baseline models including LSTM, TCN, CNN-LSTM, and STGCN. Moreover, the correlation between predictive uncertainty and actual error reaches 0.892, verifying the effectiveness and reliability of the proposed method for dam safety monitoring under complex conditions.