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。%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 data-base 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 sta-bility correctly.
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