Zerouali Bilel, Bailek Nadjem, Tariq Aqil, Kuriqi Alban, Guermoui Mawloud, Alharbi Amal H, Khafaga Doaa Sami, El-Kenawy El-Sayed M
Laboratory of Architecture, Cities and Environment, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali University of Chlef, B.P. 78C, 02180, Ouled Fares, Chlef, Algeria.
Laboratory of Mathematics Modeling and Applications, Department of Mathematics and Computer Science, Faculty of Sciences and Technology, Ahmed Draia University of Adrar, 01000, Adrar, Algeria.
Sci Rep. 2024 Sep 18;14(1):21812. doi: 10.1038/s41598-024-72588-5.
The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.
边坡稳定性评价在岩土工程中至关重要，对基础设施安全、自然灾害缓解和环境保护具有重要意义。本研究旨在确定影响边坡稳定性的最具影响力因素，并评估各种机器学习模型对边坡稳定性进行分类的性能。通过使用随机森林回归器进行相关性分析和特征重要性评估，确定了黏聚力、重度、边坡高度和摩擦角是影响边坡稳定性的最关键参数。本研究评估了机器学习技术与现代特征选择算法及传统特征分析方法相结合的有效性。评估了深度学习模型，包括递归神经网络（RNN）、长短期记忆（LSTM）网络和生成对抗网络（GAN）在边坡稳定性分类中的性能。GAN模型表现出卓越性能，总体准确率最高达到0.913，ROC曲线下面积（AUC）最高达到0.9285。将用于特征选择的二元bGGO技术与GAN模型相结合，显著提高了分类性能，与经典GAN模型相比，bGGO-GAN模型在灵敏度、阳性预测值、阴性预测值和F1分数方面均有所增强。bGGO-GAN模型在包含627个样本的大量数据集上达到了95%的准确率，在与文献中的其他模型相比时表现出竞争力，同时具有很强的泛化能力。本研究突出了先进机器学习技术和特征选择方法在改善边坡稳定性分类方面的潜力，并为岩土工程应用提供了有价值的见解。
