An improved GRU method for slope stress prediction

The stability of open-pit mine slopes is a complex nonlinear system. Stress variation is a significant influencing factor in the occurrence of landslide disasters and is also a key research focus in landslide early warning and risk assessment. However, traditional methods are confronted with challenges, including low prediction accuracy and poor robustness when dealing with nonlinear time series data. In order to address the aforementioned issues, the present paper proposes an intelligent prediction model based on Variational Mode Decomposition (VMD) and Dung Beetle Optimization (DBO), combined with an improved Gated Recurrent Unit (GRU), which is hereby referred to as the VMD-DBO-GRU-A model. The preliminary preprocessing of open pit mine slope stress data using VMD can provide high decomposition accuracy and can effectively extract localized features in the stress; The method introduces Dung Beetle Optimization (DBO) to determine the number of hidden neuron layers and the optimal learning rate for the GRU. This reduces the uncertainty of model parameters and minimizes the time required for parameter tuning; Self-attention mechanism is also added to assign different weights to the input features, which reduces the dependence on external information and is more adept at capturing the internal relevance of the data or features. In order to verify the validity of the model, experiments are conducted on a self-constructed stress dataset in this paper. The experimental results show that the root-mean-square error of the VMD-DBO-GRU-A model has decreased by 77% and 84% compared with the LSTM and SVM models, respectively, and the coefficient of determination is 0.9978, which fully verifies that the VMD-DBO-GRU-A model has an excellent comprehensive performance and high prediction accuracy, which is of great value for the practical application of landslide early warning for open-pit mines' slopes.