Slope stability prediction is a critical challenge in geotechnical engineering. This challenge arises from the complex nonlinear interactions among multiple influencing factors such as soil properties, slope geometry, and hydraulic conditions. Traditional analytical methods rely on simplified assumptions that fail to capture these complex relationships. To address these limitations, this study proposes a hybrid predictive framework that integrates machine learning models with intelligent optimization algorithms. Six key parameters, including unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio, are selected as input features. Three machine... More >

To improve the prediction accuracy of slope stability and prevent slope failure accidents, this study proposes a slope stability prediction model based on an improved pelican optimization algorithm optimized random forest (Improved Pelican Optimization Algorithm optimized Random Forest, IPOA-RF). First, according to 431 slope cases, the slope height, slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio were selected as the main predictive features. Second, due to the issue of excessive hyperparameters in the traditional random forest (RF) model, the IPOA algorithm was employed to optimize the RF parameters using an optimal-guidance strategy, mutation op... More >