Prediction of loess collapsibility coefficient using bayesian optimized random forest model

Accurately predicting the collapsibility coefficient of loess is crucial for mitigating the hazards associated with loess collapsibility in engineering projects, natural environment, and socio-economic activities. The traditional method for determining the collapsibility coefficient is time-consuming, labor-intensive, and expensive. In recent years, researchers have increasingly employed machine learning techniques to predict collapsibility coefficient and have obtained promising results. However, the process of hyperparameter optimization in previous studies was not sufficiently comprehensive, leading to suboptimal model performance. Therefore, in this study, Bayesian optimization was employed to fine-tune the hyperparameters of six different regressors, and the performance of these models was evaluated on both a training set and an independent testing set. The results demonstrated that the Random Forest-based model achieved the best performance, with R² values of 0.915 and 0.965 on the training and independent testing sets, respectively. These findings indicate that the proposed model is capable of reliably predicting the collapsibility coefficient of loess.