# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 4 Discussion  
In multi-scale landslide displacement modeling, the quality of signal decomposition directly influences the stability and interpretability of prediction results. Compared to traditional methods, such as EMD, EEMD, and wavelet analysis [14; 15; 16; 17; 18; 19; 20; 21; 22], the VMD approach adopted in this study demonstrates significant advantages. It distinctly separates displacement sequences into a long-term trend component controlled by geological structures and a high-frequency periodic component influenced by rainfall disturbances, thereby providing physically meaningful intrinsic modes for subsequent modeling. Unlike EEMD, VMD eliminates the need for multi-round perturbation superposition and reconstruction during decomposition, resulting in higher computational efficiency. This makes it more suitable for rapid early warning modeling in practical engineering scenarios. Furthermore, the framework's robust predictive performance confirms the superiority of VMD, demonstrating reliable capabilities in real-world slope monitoring applications. For predictive modeling, this study employs a hybrid \"trend component-GRU, periodic component-LSTM\" strategy, which shows notable advantages over existing approaches that uniformly apply a single network model to all components. For instance, [PERSON] et al. [30], [PERSON] et al. [31], and [PERSON] et al. [32] utilized LSTM models for full displacement sequence modeling, while [PERSON] et al. [33; 34] applied GRU models to holistically predict landslide displacement. While these methods exhibit certain predictive capabilities in handling complex temporal data, they fail to distinguish the dynamic differences between trend and periodic components, often leading to feature ambiguity or structural underfitting during modeling. Through our component-specific matching strategy, the GRU model achieves an R\({}^{2}\) value of 0.99755 in trend component prediction, significantly outperforming comparative models including ARIMA, TCN, and even LSTM (Table 7). This highlights GRU's advantages in capturing slow-varying trends with fewer parameters and faster convergence. Simultaneously, the LSTM model attains the lowest RMSE (0.0978 mm) in periodic component prediction, surpassing the traditional RNN, CNN, and GRU models (Table 9), demonstrating its strength in modeling complex periodic disturbances and high-frequency responses. The final PSO-VMD-GRU/LSTM framework achieves an overall displacement prediction R\({}^{2}\) of 0.9973, further validating the effectiveness of component-specific modeling in enhancing prediction accuracy and model generalization capabilities. Although the proposed framework is constructed based on data from the Bayan Obo open-pit iron mine, its core methodology exhibits strong transferability. Notably,  
Figure 18: Total displacement prediction results. the DBSCAN algorithm identifies early warning units through spatiotemporal density distributions of monitoring points, independent of specific geological types. This makes the method applicable to other open-pit mine slopes with dense displacement monitoring data. However, broader implementation requires careful consideration of the following prerequisites: First, the method depends on high-temporal-resolution monitoring data as foundational support. This study utilizes hourly interval, ground-based radar data comprising 114,283 monitoring points, each containing 48 h of continuous displacement records, which provide the necessary data integrity for VMD decomposition and periodic feature extraction. It should be noted that the decomposition results and subsequent prediction model performance would be significantly degraded under conditions of sparse sampling intervals, data discontinuity, or abnormal missing values. Second, the prediction of periodic components is highly dependent on rainfall as an external driver. However, the rainfall-displacement coupling relationship is not universally applicable.