Predicting the dynamic characteristics of soft soil landslides based on the random forest algorithm and physical model experiments

Abstract

[Objective] Soft soil landslides, due to their complex plastic deformation and rheological properties, have failure mechanisms distinct from ordinary landslides in hard rock and soil. Traditional method of landslide research are ineffective at capturing the nonlinear and uncertain characteristics of their movement. [Methods] To quantitatively evaluate the movement characteristics of soft soil landslides, a viscoplastic fluid assumption of soft soil was adopted, using ideal viscoplastic material Carbopol as the test material in physical model experiments. Thickness and velocity at a specified distance were chosen as representative parameters, and a prediction model was built by training the experimental data with the random forest algorithm. [Results] The results show that the determination coefficients of the thickness training and testing data were 0.941 and 0.923, respectively, while those of the velocity training and testing data were 0.936 and 0.917, respectively. The residuals for thickness and velocity were mainly distributed within the ranges of(-0.02, 0.02) and(-0.1, 0.075), respectively, and showed a normal distribution. Furthermore, an analysis of feature importance indicated that yield stress had the most significant impact on the model, with an importance value of 0.35. [Conclusion] The results demonstrate that the model has high predictive accuracy and good generalization ability, and it performs well in handling high-dimensional data and complex nonlinear relationships in the dynamics of soft soil landslides. This provides a scientific basis for the prevention and control of soft soil landslide disasters.

Keywords

soft soil landslide / random forest algorithm / viscoplastic fluid / prediction model / Carbopol / dynamic characteristics / numerical simulation / influencing factors