# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 5 Conclusions  
This study addressed the limitations of traditional landslide early warning methods in open-pit mines, specifically their insufficient consideration of spatiotemporal correlation and the challenges in modeling displacement sequences with multi-scale coupling. To tackle these issues, a novel landslide prediction framework was proposed, integrating DBSCAN clustering with PSO-VMD for multi-scale decomposition. Using the southern slope of the Bayan Obo main open-pit mine as the research area, and leveraging the spatiotemporal continuity of radar monitoring data, high-risk deformation regions were identified through DBSCAN clustering. Combined with PSO-VMD decomposition and hybrid prediction models, the framework successfully revealed the multi-scale dynamic mechanisms underlying slope displacement evolution. The main conclusions are as follows:  
1. By applying a parameter-optimized DBSCAN algorithm to radar point cloud data, Cluster 4--characterized by high average cumulative displacement and wide spatial distribution (located in the 1598-1626 m platform zone)--was successfully identified. This method effectively suppressed outlier interference, and the resulting early warning outputs closely matched the actual spatial-temporal patterns of the landslide. 2. The displacement of Cluster 4 was decomposed into trend and periodic components using VMD optimized by PSO. The trend component captured long-term displacement evolution, with the GRU model achieving a high prediction accuracy (R\({}^{2}\) = 0.99755). The periodic component reflected the nonlinear response to hydrological disturbances, and the LSTM model achieved a superior performance with RMSE = 0.0978 mm, significantly outperforming the RNN, GRU, and CNN models. The reconstructed total displacement prediction reached an R\({}^{2}\) of 0.9973, further confirming the effectiveness of the multi-scale decomposition framework. Despite the above results, several limitations remain in the present study. While the Discussion Section highlighted potential challenges in applying the proposed framework to different geological contexts, the current stage of this research still presents certain limitations--particularly in terms of model validation. The proposed framework currently relies on radar displacement curves as the primary validation source. Although manual field inspections prior to the landslide revealed surface cracking and ground bulging in the predicted high-risk area (Cluster 4), no quantitative ground-based monitoring data were available for independent verification. In future work, we plan to incorporate GNSS-based displacement measurements, geological mapping, groundwater sensors, and crack meters to further enhance the model's stability and generalizability under complex geological and hydrological conditions. Moreover, future developments may explore embedding the framework into real-time monitoring systems to support timely decision-making in mine slope management. The proposed \"Spatiotemporal Clustering-Multi-Scale Decomposition-Hybrid Prediction\" framework enables the identification of landslide-prone regions on larger spatial scales and enhances both the accuracy and robustness of displacement prediction. This method offers a promising new technical solution for improving precursor identification of slope instability and ensuring the safety of slope monitoring in open-pit mining operations. Methodology, P.Z., Y.L. and H.L. ; software, P.Z., Y.L. and X.D. ; validation, P.Z. and Y.L. ; formal analysis, P.Z., Y.L. and X.D. ; investigation, P.Z., Y.L. and T.Y; resources, Y.L., T.Y. and H.L. ; data curation, Y.L., X.D. and H.L. ; writing--original draft, Y.L. ; writing--review and editing, P.Z. and T.Y. ; supervision, P.Z. and X.D. All authors have read and agreed to the published version of the manuscript. The work presented in this paper was financially supported by the National Key R&D Program of China (Grant No. 2022 YFC2903903) and the National Natural Science Foundation of China (Grant No. 52174070). Not applicable.