What question did this study set out to answer?

The aim is to enhance prediction accuracy of slope displacement using a new model combining CNN and LSTM techniques with Bayesian optimization.

February 28, 2026Open Access

Multi-Modal Data-Driven Bayesian-Optimized CNN-LSTM Model for Slope Displacement Prediction

Key Points

The aim is to enhance prediction accuracy of slope displacement using a new model combining CNN and LSTM techniques with Bayesian optimization.
Constructed a Bayes-CNN-LSTM predictive model using multi-modal monitoring data.
Evaluated model performance using several metrics including R2, MAE, and RMSE.
Compared the model against several existing methods including CNN, LSTM, and SVM.
Achieved an R2 of 0.971, with MAE of 0.444 mm and RMSE of 0.618 mm.
Improved MAE by 25.1% to 32.3% compared to other models.
Demonstrated 30.2% reduction in MAE and 24.6% in RMSE for 24-hour prediction using rainfall and earth pressure data.

Abstract

Accurate prediction of slope displacement is an important prerequisite for building an effective geological hazard early warning system for disaster prevention and reduction. However, the inherent nonlinearity and time-varying characteristics of slope displacement evolution greatly affect the prediction accuracy. To improve the slope displacement prediction accuracy, a multi-modal data-driven Bayesian-optimized Convolutional Neural Network and Long Short-Term Memory (Bayes-CNN-LSTM) model was constructed. The performance of the model was evaluated using multi-modal monitoring data from the GuShan mine slope. Experimental results showed that the Bayes-CNN-LSTM model achieved an average coefficient of determination (R2) of 0.971, with a mean absolute error (MAE) of 0.444 mm and a root mean square error (RMSE) of 0.618 mm. Compared with the CNN-LSTM, LSTM, CNN, SVM, TCN, and Transformer models, the MAE of the constructed model was decreased by 25.1%, 31.3%, 32.3%, 24.1%, 24.7%, and 17.7%, respectively, and the RMSE decreased by 20.1%, 26.9%, 29.5%, 18.0%, 20.7%, and 12.4%, respectively. Furthermore, the proper integration of multi-modal data can effectively improve the prediction accuracy when extrapolating slope displacement. Based on rainfall and earth pressure data, the average MAE and RMSE of extrapolation (24-h) prediction using the constructed model were decreased by 30.2% and 24.6%, respectively. The model effectively improves the accuracy of slope displacement prediction and enhances the practicality of the slope safety monitoring system, providing valuable reference for slope safety monitoring.

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