基于VMD-XGBoost-GRU模型的危岩体变形预测

针对以往对危岩体监测数据前处理效果不佳的问题，提出了一种用于危岩体变形预测的VMD-XGBoost-GRU组合模型。该模型采用变分模态分解(VMD)和样本熵理论将危岩体变形数据分解成多个子序列，利用XGBoost算法提取重要的模型因子实现特征降维，通过门控循环单元(GRU)神经网络对危岩体变形进行预测。以某水电站右坝肩陡壁上的危岩体变形预测为例，将VMD-XGBoost-GRU组合模型与BP、GRU和VMD-XGBoost-BP 3种模型进行对比与分析，结果表明，VMD-XGBoost-GRU组合模型在危岩体变形预测方面具有较高精度，可为危岩体安全稳定状态评价提供技术依据。

Deformation prediction of dangerous rock mass based on VMD-XGBoost-GRU model

Aiming at the problem of poor pre-processing of monitoring data of dangerous rock mass in the past, a VMD-XGBoost-GRU combined model was proposed for predicting deformation of dangerous rock mass. The model firstly uses variational mode decomposition (VMD) and sample entropy theory to decompose the deformation data of dangerous rock mass into multiple sub-sequences, then uses extreme gradient lift (XGBoost) algorithm to extract important model factors to achieve feature dimensionality reduction, and finally predicts the deformation of dangerous rock mass through gated recurrent unit (GRU) neural network. Taking the dangerous rock mass on the steep wall of the right abutment of a hydropower station as an example, the VMD-XGBoost-GRU combined model was compared with three other models, namely BP, GRU and VMD-XGBoost-BP. The results show that the VMD-XGBoost-GRU combined model has high precision in predicting the deformation of dangerous rock mass, and can provide technical basis for the evaluation of the safe and stable state of dangerous rock mass.