High-precision prediction method for mine deformation based on GNSS RTK and stacking ensemble learning

To address the demand for high-precision deformation monitoring during mine exploitation, this paper proposes a high-precision mine deformation prediction method based on stacking ensemble learning, which leverages Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) data. First, a fusion filtering preprocessing module integrating median filtering, Butterworth filtering, Savitzky-Golay filtering, and the Adaptive Kalman Filter (AKF) is established to suppress various types of noise in the original data. Second, a cumulative deformation time series is constructed and decomposed into trend, seasonal, and residual components. Concurrently, the deformation rate and acceleration are calculated, with all these variables jointly serving as input features for the deformation prediction model. Finally, a stacking ensemble module is developed by integrating three time series models and four machine learning models, where Elastic Net Regression (ENR) is employed as the meta-model to realize dynamic weight optimization, thereby enabling high-precision prediction of cumulative deformation. Experimental results demonstrate that the fusion filtering preprocessing significantly improves the quality of the original data. Additionally, under different prediction time scales of the measured dataset, the Root Mean Squared Error (RMSE) of predictions generated by the stacking ensemble module can be maintained below 0.3 mm, and the module exhibits excellent trend consistency and response capability. In summary, the proposed method provides efficient and reliable technical support for the active prevention and control of mine deformation disasters.