
鉴于边坡特征之间、特征与稳定性判定之间的复杂非线性关系,经典的高斯过程边坡稳定性预测方法在复杂结构建模上表现有限且难以处理大规模的边坡数据,提出一种结合深度核学习与高斯过程的边坡稳定性预测方法。首先,利用多层前馈网络对边坡特征进行深度提取,再将隐空间映射到带有径向基函数核的高斯过程,实现非参数不确定性量化。模型通过最大化边缘对数似然函数优化神经网络权重与核超参数,可端到端学习数据驱动的最优核。在公开的Kaggle数据集上的试验表明,所提方法较经典机器学习算法随机森林RF、支持向量机SVM、高斯过程回归GPR
以及深度学习方法门控循环单元GRU、深度神经网络DNN在均方根误差、平均绝对误差和决定系数等指标上均取得最佳结果,为边坡灾害智能预警提供了新的技术支撑。
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