Based on the machine learning algorithms, prediction models for landslide displacement with step-like behavior in the reservoir area were established for landslides prevention and reduction; these models could predict a given test set very well. However, due to the length and the sequence of the training set in prediction models, the predictive ability of these prediction models could not be evaluated accurately if just validated with a given test set. To solve the problem, a hybrid reliability model was proposed. Complimentary ensemble empirical mode decomposition (CEEMD) algorithm was used to decompose the accumulated displacement into the trend displacement and the periodic displacement firstly. The Gauss function was used to predict the trend displacement, and the random forest (RF) and back propagation neural network (BPNN) algorithms were employed to predict the periodic displacement. Furthermore, a novel performance function for the reliability analysis of the displacement prediction model was derived to address failure probabilities in different cases. The Baijiabao landslide in the Three Gorges Reservoir Area was taken as an example for reliability analysis of the prediction model for landslide displacement with step-like behavior, and the predictive ability of the CEEMD-RF model and the CEEMD-BPNN model were compared. The results indicated that the CEEMD-RF model and CEEMD-BPNN model both can accurately predict the accumulated displacement of the given test set; the predictive value obtained with the CEEMD-RF model or the CEEMD-BPNN model showed uncertainties of the prediction model for landslide displacement and the predictive ability of the CEEMD-RF model was more reliable than the CEEMD-BPNN model under different cases. The failure probability proposed in the paper could evaluate the predictive ability of the model more accurately and comprehensively compared with the existing assessment indices.

基于机器学习算法，建立了库区滑坡位移具有阶梯状行为的预测模型，以预防和减少滑坡。这些模型可以很好地预测给定的测试集。但是，由于预测模型中训练集的长度和顺序，如果仅使用给定的测试集进行验证，则无法准确评估这些预测模型的预测能力。为了解决这个问题，提出了一种混合可靠性模型。首先采用互补集成经验模式分解（CEEMD）算法将累积位移分解为趋势位移和周期位移。高斯函数用于预测趋势位移，并采用随机森林（RF）和反向传播神经网络（BPNN）算法预测周期位移。此外，推导了用于位移预测模型可靠性分析的新型性能函数，以解决不同情况下的失效概率。以三峡库区白家堡滑坡为例，对具有阶梯状行为的滑坡位移预测模型进行了可靠性分析，比较了CEEMD-RF模型和CEEMD-BPNN模型的预测能力。结果表明，CEEMD-RF模型和CEEMD-BPNN模型都可以准确预测给定测试集的累积位移。用CEEMD-RF模型或CEEMD-BPNN模型获得的预测值表明滑坡位移预测模型的不确定性，并且在不同情况下，CEEMD-RF模型的预测能力比CEEMD-BPNN模型更可靠。与现有的评估指标相比，本文提出的失效概率可以更准确，更全面地评估模型的预测能力。