基于RF-ELM模型的边坡稳定性预测研究

为实现对边坡稳定性的有效预测，将极限学习机算法与旋转森林算法相结合，并依据影响边坡稳定性的六项重要因素，建立了边坡稳定性预测的RF-ELM预测模型。该模型是以极限学习机算法为基分类器，以旋转森林算法为框架的集成学习模型，利用UCI数据库中三组数据集验证了该集成模型确实提高了ELM的预测性能。将RF-ELM模型应用于边坡稳定性的预测问题中，结合39组工程实例数据进行预测实验，结果表明该模型具有较高的预测精度，可有效的对边坡稳定性进行预测。

Study on slope stability prediction based on RF-ELM model

In order to predict the slope stability effectively, considering the six important influence factors of slope stability, a RF-ELM forecasting model of slope stability was established by combining extreme learning algorithm and rotation forest algorithm. This model is an integrated learning method, which uses extreme learning algorithm as base classifier and rotation forest algorithm as integration framework. A prediction test on 3 data sets of UCI database proved that the model can improve the prediction performance of ELM. By applying RF-ELM model in slope engineering, the prediction experiments were conducted on 39 groups of data in engineering cases. The results showed that RF-ELM model has a higher forecasting accuracy, and it is an effective model for predicting slope stability correctly.