In this work, we proposed a deeply-integrated explainable pre-trained deep learning framework with stacked denoising autoencoders in the assessment of slope stability. The deep learning model consists of a deep neural network as a trunk net for prediction and autoencoders as branch nets for denoising. A comprehensive review of machine learning algorithms in slope stability evaluation is first given in the introduction section. A series of 530 data is then collected from real slope records, which are visualized and investigated in feature engineering and further preprocessed for model training. To ensure reliable and trustworthy model interpretability, a unified model from both local and global perspectives is integrated into the deep learning model, which incorporated the ad hoc back-propagation based Deep SHAP, perturbation based Kernel SHAP and PDPs, and distillation based LIME and Anchors. For a fair evaluation, repeated stratified 10-fold cross-validation is adopted in model evaluation. The obtained results manifest that the constructed model outperforms commonly used machine learning methods in terms of accuracy and stability on the real-world slope data. The explainable model provides a reasonable explanation and validates the capability of the proposed model, and reflects the causes and dependencies of model predictions for a given sample.

中文翻译：

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用于边坡稳定性分析的具有堆叠去噪自动编码器的预训练可解释深度学习模型

在这项工作中，我们提出了一种深度集成的可解释的预训练深度学习框架，具有堆叠式去噪自动编码器，用于评估边坡稳定性。深度学习模型由作为预测主干网络的深度神经网络和作为去噪分支网络的自动编码器组成。引言部分首先对边坡稳定性评估中的机器学习算法进行了全面回顾。然后从真实坡度记录中收集一系列 530 个数据，在特征工程中对这些数据进行可视化和研究，并进一步预处理以进行模型训练。为了确保可靠且值得信赖的模型可解释性，深度学习模型中集成了局部和全局视角的统一模型，其中包含基于临时反向传播的 Deep SHAP、基于扰动的 Kernel SHAP 和 PDP，以及基于蒸馏的 LIME 和 Anchors 。为了公平评估，模型评估采用重复分层10倍交叉验证。获得的结果表明，所构建的模型在真实坡度数据的准确性和稳定性方面优于常用的机器学习方法。可解释模型提供了合理的解释并验证了所提出模型的能力，并反映了给定样本的模型预测的原因和依赖性。