A Novel LOF–KNN–Bessel Approach for Optimizing and Predicting Slope Deformation Monitoring Data: A Case Study of the Shilu Iron Mine

Slopes transitioning from open-pit to underground mining typically exhibit heterogeneous and nonlinear deformation characteristics. Under complex environmental disturbances, monitoring data are often affected by high noise and outliers, making it difficult to accurately capture critical deformation characteristics and posing challenges for landslide early warning and safety assessment. Therefore, it is necessary to develop a high-precision data optimization technique suitable for complex, high-noise monitoring time series data to improve slope stability evaluation and the robustness of prediction algorithms. Based on slope deformation monitoring data from the Hainan Shilu Iron Mine, the multi-type, nonlinear, and alternating acceleration-deceleration patterns of deformation time series data were analyzed, and the performances of multiple anomaly detection and interpolation compensation algorithms were compared. The results show that the Local Outlier Factor (LOF) and K-Nearest Neighbors (KNN) algorithms achieve better performance in processing noisy and dynamically varying time series data based on comparative evaluations of detection accuracy and interpolation error. Furthermore, a Bessel function-based denoising technique was proposed for landslide monitoring systems. This technique effectively filters high-frequency noise while preserving the main characteristics of the data and outperforms conventional methods, including the Moving Average Method (MAM), Triple Exponential Smoothing (TES), and Least Squares Method (LSM). The proposed technique, integrating LOF-based anomaly detection, KNN-based interpolation compensation, and Bessel function denoising, can effectively process slope deformation monitoring data characterized by multi-type, nonlinear, and alternating acceleration-deceleration patterns. Engineering application at the Hainan Shilu Iron Mine demonstrated that the proposed technique improves data quality and model prediction performance, providing valuable support for slope stability analysis and disaster early warning systems in slopes transitioning from open-pit to underground mining.