Research on Landslide Displacement Prediction Using Stacking-Based Machine Learning Fusion Model

To address the issues of the insufficient accuracy and weak generalization capabilities of single models in landslide displacement prediction, this paper proposes a machine learning model fusion prediction method for landslide displacement based on stacking. Taking the landslide displacement data (F) and rainfall (RAINFALL) of the Baishui River landslide in the Three Gorges Reservoir area as the research object, input sequences were constructed through data preprocessing and feature engineering. Prediction models including SVR, XGBoost, Bayesian optimization, and random forest were established. Based on the stacking framework, an integrated landslide displacement prediction model was developed by dynamically weighting the outputs of the base models using prediction accuracy and stability as fusion indicators. The Baishui River landslide, a typical colluvial landslide, was selected as a case study, with typical displacement data from monitoring points ZG118 and XD-01 from December 2006 to December 2012. The results show that the evaluation metrics (R2, ERMSE, and EMAE) for ZG118 and XD-01 demonstrate satisfactory prediction performance. Compared with traditional single models such as a TCN and XGBoost, the proposed integrated model exhibits improved prediction accuracy, providing scientific support for the real-time monitoring and early warning of landslide hazards.