A Multi-Source Monitoring Data Fusion and Early Warning Framework Based on LightGBM-SHAP and Weibull Distribution
This paper addresses the need for multi-source data fusion and risk early warning in slope monitoring by proposing a modeling method that integrates data calibration, feature interpretation, and hierarchical early warning. First, linear regression is used to calibrate fiber-optic displacement data, and data consistency is improved through anomaly removal, multiple interpolation, and wavelet denoising. Subsequently, a LightGBM regression model is constructed to characterize the nonlinear relationship between surface displacement and factors such as rainfall, pore water pressure, microseismic events, and deep-seated displacement. The SHAP method is introduced to explain the contributions of each factor, thereby enhancing the interpretability of the model results. Building on this, Lasso regression is used to screen for key variables and reduce redundant information, followed by the establishment of a probabilistic representation of displacement velocity and graded early warning thresholds based on the Weibull distribution. This method balances prediction accuracy, physical plausibility, and engineering applicability, and can serve as a reference for identifying complex slope deformations, analyzing key controlling factors, and making risk warning decisions.