基于CEEMD-RF模型的渣土边坡地下水埋深预测

地下水是影响渣土边坡稳定性的关键因素之一，地下水埋深预测对分析渣土边坡稳定性具有重要意义。考虑渣土边坡地下水水位的高度非平稳和非线性特点，提出了一种基于相空间重构的互补集合经验模态分解-随机森林（CEEMD-RF）的地下水埋深预测模型。以广州市某渣土边坡SW2水文观测孔为例，将基于相空间重构的CEEMD-RF模型应用于该渣土边坡的地下水埋深预测，并与相空间重构的RF模型预测结果进行对比分析。结果表明:利用CEEMD-RF模型对地下水埋深预测的拟合优度为0.997，均方根误差为0.03 m，优于相空间重构的RF模型预测结果；基于相空间重构的CEEMD-RF模型预测的地下水埋深序列能很好地反映地下水埋深的尖变点。

Prediction of groundwater depth in residue slope with CEEMD-RF model based on phase space reconstruction

Groundwater is one of the key factor affecting the stability of residue slope, and the prediction of groundwater depth in residue slope plays a significance role in analyzing the slope stability. A prediction model of groundwater depth, namely complementary ensemble empirical mode decomposition-random forest (CEEMD-RF) model based on phase space reconstruction, is proposed considering the highly non-stationary and nonlinear characteristics of groundwater level in residue slope. The CEEMD-RF model is applied to the groundwater depth prediction of SW2 hydrological observation hole of a residue slope in Guangzhou and the prediction results are compared and analyzed with that calculated by phase space reconstruction-RF model. The result shows that the fitting goodness of the groundwater depth predicted by CEEMD-RF model is 0.997, the root mean square error (RMSE) is 0.03 m, which is better than those using phase space reconstruction-RF model. The predicted groundwater depth series based on the CEEMD-RF model reflects the sharp point very well.