Slope Displacement Prediction Using MIC-XGBoost-LSTM Model

A long short-term memory (LSTM) neural network model for predicting slope displacements based on maximum mutual information coefficients (MICs) and the XGBoost algorithm (MIC-XGBoost LSTM) was established to accurately predict slope displacements. First, the effects of different rainfall conditions on the slope were investigated. The maximum MIC was used to analyze the correlation between different rainfall conditions and the cumulative displacement of the slope, and the rainfall-influencing factors with significant correlations were determined. Next, based on the XGBoost algorithm, feature construction was performed on the influencing factors with high correlation using the cumulative displacement data of the slope, and the construction features were normalized with the original features. The normalized data were divided into training and validation sets. LSTM was used to predict the displacement of the Shangluo rock slope on the G312 National Highway. The XGBoost, LSTM, and MIC-XGBoost-LSTM prediction models were used to train and predict the cumulative displacement value of the slope, and the prediction accuracy was evaluated based on the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators. In addition, the RMSE was used to determine the longest prediction cycle and minimum training sample size for the MIC-XGBoost LSTM model. Finally, the displacement data of the Baishui River landslide were used to further validate the model. The results show that the correlations between daily displacement increment, evapotranspiration, net rainfall, cumulative seven-day rainfall, and cumulative displacement at the monitoring point are higher than those of other factors, and the MIC of the feature values constructed using four related factors and the output feature values is 0. 97. The RMSE, MAE, and (MAPE) of the predicted results obtained using the MIC-XGBoost-LSTM model are 0. 25%, 0. 185%, and 0. 024%, respectively, which arc lower than those of XGBoost and LSTM. Based on the RMSE, the longest prediction cycle and minimum training sample size of the MIC-XGBoost-LSTM model arc 56 and 675, respectively. Finally, the displacement data of the Baishui River landslide were used for verification. The evaluation indicators arc lower than those of the XGBoost and LSTM models, demonstrating that the MIC-XGBoost-LSTM slope displacement prediction model has high reliability. © 2024 Chang'an University. All rights reserved.