# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 1 Introduction
[25] proposed a dynamic interval prediction method for landslide displacement based on the random forest algorithm, which enhanced prediction accuracy and reliability by automatically identifying deformation states and incorporating a state-transition model. [PERSON] et al. [26] developed a regression-based machine learning approach using 3D photogrammetric models of open-pit highwalls to rapidly predict rock-fall energy and run-out distances. [PERSON] et al. [27] applied ensemble learning techniques (RF and XGBoost) to slope stability prediction, achieving higher accuracy compared to support vector machines and logistic regression. [PERSON] et al. [28] employed a hybrid stacking ensemble method optimized by the artificial bee colony (ABC) algorithm, integrating finite element-derived synthetic data with 107 field cases, and achieved a prediction AUC of 90.4%, significantly outperforming single machine learning models (maximum AUC 82.9%) and basic ensemble methods. However, traditional machine learning models remain essentially static, suffering from limited modeling capacity, an inability to capture temporal dependencies, and high computational complexity. With the rapid advancement of deep learning technologies, LSTM and Gated Recurrent Unit (GRU) models have overcome the gradient vanishing and explosion problems associated with recurrent neural networks (RNNs). These models, which process data sequentially over time, are well suited for time series prediction tasks and have seen widespread application in various fields in recent years [29]. [PERSON] et al. [30] employed LSTM to predict the periodic displacement of landslides, finding that LSTM exhibited excellent dynamic properties. [PERSON] et al. [31] applied LSTM in displacement prediction for the Baishuile landslide, demonstrating that LSTM outperformed the Extreme Learning Machine (ELM) in terms of prediction accuracy. [PERSON] et al. [32] applied LSTM to landslide displacement analysis, revealing that compared to static models, LSTM better captured the dynamic characteristics of landslides and effectively utilized historical data. [PERSON] et al. [33] developed a landslide displacement prediction model using GRU and applied it to the Diaohuo landslide in the Three Gorges area, showing that GRU offeredhigher prediction accuracy than the Support Vector Machine (SVM). [PERSON] et al. [34] further applied GRU to predict displacement at the Jiuxianping landslide, demonstrating its superior performance in capturing dynamic displacement features and providing a more accurate representation of the Jiuxianping landslide's deformation, with fewer outliers compared to static models. Inspired by previous studies, this research applies both LSTM and GRU models to displacement prediction and further introduces representative models, such as Temporal Convolutional Network (TCN), RNN, and Autoregressive Integrated Moving Average (ARIMA), for comparative analysis, aiming to systematically evaluate the performance of different modeling approaches in capturing displacement patterns. This study focuses on the south slope of the main pit in the Bayan Obo open-pit mine. After identifying early warning units using the DBSCAN algorithm, the landslide displacement data of these units are decomposed by VMD into trend and periodic components with clear physical significance. The GRU model is then used to predict the trend component, while the LSTM model is applied to the periodic component. The final displacement prediction is obtained through weighted reconstruction to improve overall accuracy. Accordingly, the objective of this study is to address the high rates of false and missed alarms by fully considering the spatiotemporal distribution characteristics of slope deformation. A displacement prediction framework is established to improve forecasting accuracy and to assign explicit physical meanings to the decomposed components.