水力发电2026，Vol.52Issue(2)：40-47,8.
结合深度核学习与高斯过程的边坡稳定性预测方法
Slope Stability Prediction Combining Deep Kernel Learning and Gaussian Process
摘要
Abstract
Given the complex nonlinear relationships among slope features and between these features and stability evaluation,classical Gaussian process-based slope stability prediction methods are limited in modeling intricate structures and are difficult to scale to large slope datasets.A slope stability prediction method combining deep kernel learning and Gaussian process is therefore proposed.A multilayer feedforward network first performs deep extraction of slope features,and the latent space is then mapped into a Gaussian process with a radial basis function kernel to realize nonparametric uncertainty quantification.By maximizing the marginal log-likelihood,the model jointly optimizes neural network weights and kernel hyperparameters,enabling end-to-end learning of a data-driven optimal kernel.Experiments on a public Kaggle dataset show that the proposed method surpasses classical machine learning algorithms of Random Forest(RF),Support Vector Machine(SVM),and Gaussian Process Regression(GPR),and the deep learning methods of Gated Recurrent Unit(GRU)and Deep Neural Network(DNN),achieving the best performance in root mean square error,mean absolute error,and coefficient of determination,thus providing new technical support for intelligent early warning of slope hazards.关键词
边坡稳定性/预测算法/深度核学习/高斯过程回归/经典机器学习算法Key words
slope stability/prediction algorithm/deep kernel learning/Gaussian Process Regression/classical machine learning algorithm分类
建筑与水利引用本文复制引用
李书,喻国荣,付兵杰,鲍海洲..结合深度核学习与高斯过程的边坡稳定性预测方法[J].水力发电,2026,52(2):40-47,8.基金项目
国家自然科学基金资助项目(42501559) （42501559）
湖北省自然科学基金资助项目(2025AFB544) （2025AFB544）
长江勘测规划设计研究有限责任公司自主创新资助项目(CX2023Z34-1) （CX2023Z34-1）
