Slope stability prediction via TrAdaBoost transfer learning: integrating physics and data into a double-driven framework.

Slope stability prediction is a crucial aspect of landslide disaster prevention and control. Traditional methods are physics-based or data-driven, which are limited by their respective reliance on geotechnical engineering expertise and scarce real-world slope data. This study developed a slope stability prediction model that integrates physics and data through an innovative transfer learning (PD-TL) framework. Four TrAdaBoost transfer learning models were developed using Support Vector Classification (SVC), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) as base learners. At the data level, the finite element analysis cases significantly expanded the sample size to resolve issues with data scarcity and model performance limited by sample size. At the model level, most traditional data-driven methods assume that training and test datasets follow the same distribution; in contrast, the proposed model relaxes this assumption and thus offers a promising solution to the scarcity of training data. At the knowledge level, the model incorporates geotechnical engineering expertise to enhance performance by leveraging field-specific insights and principles. Among the TrAdaBoost (-SVC, LR, DT, RF) models tested, TrAdaBoost-SVC showed superior slope stability prediction performance (ACC = 0.878, Precision = 0.794, Recall = 0.931, F1 = 0.857, AUC = 0.928, TNR = 84%). Its superiority demonstrates the value of integrating physics and data into a double-driven framework, which outperforms traditional data-driven and ensemble learning methods. The results also may represent suggestions for future slope stability analysis and broader engineering applications.