A hybrid deep learning-Bayesian optimization model for enhanced slope stability classification

Ahmed Allazem, Eltayeb Mohamedelhassan,
A hybrid deep learning-Bayesian optimization model for enhanced slope stability classification,
Geodata and AI,
Volume 5,
2025,
100030,
ISSN 3050-483X,
https://doi.org/10.1016/j.geoai.2025.100030.
(https://www.sciencedirect.com/science/article/pii/S3050483X25000292)
Abstract: The process of classifying slopes according to their resistance to failure is important in the geotechnical engineering field for ensuring the safety of infrastructure and human life. This research aims to develop a novel Deep Learning-Bayesian Optimization (DL-BO) model for slope stability classification by determining the best model’s hyperparameters. The study focuses on utilizing a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and attention mechanism-enhanced LSTM (Attention-LSTM). In addition, this study applies a Bayesian optimization technique for hyperparameters tuning by using a Gaussian Process (GP) with Expected Improvement (EI) acquisition function to create the non-expensive surrogate model. The data used in the study (Appendix A) consisted of 575 real-life slope samples. The dataset was split into 85:15 training and testing sets, respectively. Five-stratified k-fold cross-validation was used to validate the DL-BO models. The dataset consisted of soil and slope characteristics, including unit weight kN/m3, cohesion (kPa), angle of internal friction (degrees), pore water pressure ratio, slope height (m), and slope angle (degrees). Widely used statistical metrics in the research literature were applied to evaluate the DL-BO models, including accuracy, precision, recall, specificity, F1-score, and Area Under the ROC Curve (AUC). RNN-BO and LSTM-BO achieved significant model accuracy, reaching 81.6 % and 85.1 %, and AUC reaching 89.3 % and 89.8 %, respectively. While Bi-LSTM-BO enhanced the LSTM model, reaching an accuracy of 87.4 % and an AUC of 95.1 %, Attention-LSTM-BO arrived at an accuracy of 86.2 % and an AUC of 89.6 %. The rest of the outcomes are presented in the results and discussion section.
Keywords: Deep learning; Slope stability; Bayesian optimization